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• Published: 20 January 2022

AI in health and medicine

• Pranav Rajpurkar   ORCID: orcid.org/0000-0002-8030-3727 1   na1 ,
• Emma Chen 2   na1 ,
• Oishi Banerjee 2   na1 &
• Eric J. Topol   ORCID: orcid.org/0000-0002-1478-4729 3  

Nature Medicine volume  28 ,  pages 31–38 ( 2022 ) Cite this article

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• Computational biology and bioinformatics
• Medical research

Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human–AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI’s potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.

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Acknowledgements

We thank A. Tamkin and N. Phillips for their feedback. E.J.T. receives funding support from US National Institutes of Health grant UL1TR002550.

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These authors contributed equally: Pranav Rajpurkar, Emma Chen, Oishi Banerjee.

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Department of Biomedical Informatics, Harvard University, Cambridge, MA, USA

Pranav Rajpurkar

Department of Computer Science, Stanford University, Stanford, CA, USA

Emma Chen & Oishi Banerjee

Scripps Translational Science Institute, San Diego, CA, USA

Eric J. Topol

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P.R. and E.J.T. conceptualized this Review. E.C., O.B. and P.R. were responsible for the design and synthesis of this Review. All authors contributed to writing and editing the manuscript.

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Correspondence to Eric J. Topol .

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Rajpurkar, P., Chen, E., Banerjee, O. et al. AI in health and medicine. Nat Med 28 , 31–38 (2022). https://doi.org/10.1038/s41591-021-01614-0

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Enhancing Medical Education with Artificial Intelligence Technologies – A Comprehensive PowerPoint Presentation

• Post author By aqua
• Post date 03.12.2023
• No Comments on Enhancing Medical Education with Artificial Intelligence Technologies – A Comprehensive PowerPoint Presentation

Artificial intelligence (AI) is revolutionizing various industries, and the field of medicine is no exception. With the help of AI, medical education is becoming more efficient and effective. One of the most popular tools used for presentations in the medical field is PowerPoint (PPT), and combining it with the power of artificial intelligence can lead to tremendous improvements.

By incorporating AI into PowerPoint presentations, medical educators can create interactive and engaging content. AI algorithms can analyze the data, identify patterns, and suggest relevant images, videos, and graphs to enhance the learning experience. This not only makes the presentations visually appealing but also helps in conveying complex medical concepts in a simplified and understandable manner.

Another advantage of using AI in medical education is the ability to personalize the learning experience for individual students. With AI-powered PowerPoint presentations, educators can analyze the learning patterns and preferences of each student. Based on this analysis, the presentations can be tailored to meet the unique needs and learning styles of the students, making the educational content more accessible and impactful.

In conclusion, the integration of artificial intelligence into PowerPoint presentations has the potential to greatly enhance medical education. By utilizing AI algorithms, educators can create interactive and personalized content that improves the understanding and retention of medical concepts. This combination of AI and PowerPoint is revolutionizing the way medical education is delivered, ensuring that future medical professionals are equipped with the knowledge and skills they need to provide quality patient care.

Overview of Artificial Intelligence

Artificial intelligence (AI) is a rapidly growing field that is revolutionizing various industries, including education. In the context of medical education, AI has the potential to greatly enhance the learning experience for both students and teachers.

What is Artificial Intelligence?

Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It encompasses a wide range of technologies and techniques, including machine learning, natural language processing, and computer vision.

The Role of AI in Education

In the field of education, AI can be utilized to develop intelligent tutoring systems, personalized learning platforms, and automated grading systems. These technologies can provide personalized feedback, track student progress, and offer adaptive learning experiences.

AI-powered education tools can analyze large amounts of data and provide insights to improve instructional design and curriculum development. By identifying knowledge gaps and individual student needs, AI can help educators tailor their teaching methods and resources accordingly.

Benefits of AI in Medical Education

In the context of medical education, AI can offer several advantages. It can provide students with interactive learning experiences, such as virtual patient simulations, that allow them to practice diagnosing and treating medical conditions in a controlled environment.

AI can also assist in the assessment of students’ knowledge and skills, providing objective and standardized evaluations. Additionally, AI can enable continuous learning by recommending relevant educational resources and personalized study plans based on individual learning patterns and preferences.

Furthermore, AI can support medical educators by automating administrative tasks, such as grading assignments and organizing educational materials. This allows educators to focus more on providing personalized guidance and mentoring to students.

In conclusion , artificial intelligence has the potential to revolutionize medical education by enhancing learning experiences, providing personalized feedback and support, and automating administrative tasks. Utilizing AI technologies in medical education can ultimately help improve the quality of healthcare professionals’ training and contribute to better patient outcomes.

Challenges in Medical Education

The field of medical education faces several challenges that can be addressed with the help of artificial intelligence (AI). These challenges include:

• Limited access to quality education: Many aspiring medical students, especially those in remote areas, may not have access to high-quality educational resources. AI can be used to develop interactive and accessible learning materials, such as virtual simulations and online courses, to bridge this gap.
• Rapidly evolving medical knowledge: Medical knowledge is constantly expanding and changing. It can be difficult for educators to keep up with the latest developments and teach students effectively. AI-powered systems can assist in curating and updating educational content, ensuring that students receive the most up-to-date information.
• Lack of personalized learning: Medical education often follows a one-size-fits-all approach, which may not cater to the individual learning needs of students. AI can analyze students’ learning patterns and preferences to deliver personalized learning experiences, helping them grasp complex concepts more effectively.
• Assessment and feedback: Traditional assessment methods in medical education, such as written exams, may not fully capture a student’s understanding and clinical skills. AI can be used to develop intelligent assessment tools that provide objective feedback and support, enhancing the evaluation process.
• Integration of real-world experiences: Medical education can be theoretical, and students may lack exposure to real-world clinical scenarios. AI can simulate realistic patient cases and scenarios, allowing students to gain practical experience and enhance their decision-making skills.

By addressing these challenges, AI has the potential to revolutionize medical education and improve the quality of training provided to future healthcare professionals. It can make education more accessible, engaging, personalized, and effective.

Benefits of Artificial Intelligence in Medical Education

Artificial intelligence (AI) is revolutionizing the medical education industry, providing numerous benefits for both students and educators. By utilizing AI technology, medical education is being enhanced and expanded in ways never seen before.

1. Enhanced Learning Experience

AI-powered tools, such as virtual patient simulators and interactive learning platforms, offer medical students a more realistic and immersive learning experience. These tools simulate real-life scenarios, allowing students to practice diagnostic and treatment skills in a safe and controlled environment. This leads to better retention of knowledge and improved clinical decision-making abilities.

2. Personalized Education

AI algorithms can analyze vast amounts of data on individual student performance and devise personalized learning plans. This enables educators to cater to the unique needs and learning styles of each student, ensuring a more effective and tailored educational experience. With AI, students can receive targeted feedback, track their progress, and focus on areas that require improvement.

In conclusion, the integration of artificial intelligence into medical education brings numerous benefits, from enhanced learning experiences to personalized education. AI has the potential to revolutionize the way medical professionals are educated, ensuring better patient care and outcomes in the future.

Role of Technology in Medical Education

The field of medical education has been greatly impacted by advances in technology and artificial intelligence. These developments have revolutionized the way medical knowledge is acquired, disseminated, and applied.

Enhanced Learning Experience

Technology has made it possible to create immersive learning experiences for medical students. Virtual reality and simulation tools allow students to explore realistic medical scenarios and practice procedures in a risk-free environment. This hands-on approach enhances their understanding of complex medical concepts.

Access to Information

Artificial intelligence has made accessing medical information quicker and easier. Medical textbooks have been digitized, giving students instant access to a vast amount of knowledge. AI-powered search engines and medical databases provide accurate and up-to-date information on diagnoses, treatment options, and drug interactions.

Additionally, online platforms and educational apps allow students to learn at their own pace and access educational resources from anywhere in the world.

Improved Diagnosis and Treatment

The use of artificial intelligence in diagnostic tools has greatly improved medical decision-making. AI algorithms can analyze medical images, such as X-rays and MRIs, detecting patterns and anomalies that might be missed by human observers. This technology helps healthcare professionals make more accurate diagnoses and develop tailored treatment plans.

Furthermore, machine learning algorithms can analyze large amounts of medical data, identifying trends and patterns that enable more effective treatment strategies. This technology has the potential to accelerate medical research and improve patient outcomes.

Overall, the role of technology in medical education has transformed the way medical students learn and the way healthcare professionals practice medicine. It has enhanced the learning experience, provided easy access to information, and improved diagnosis and treatment methods. As technology continues to advance, the future of medical education holds even more promising possibilities.

Integration of Artificial Intelligence in Medical Curriculum

In the field of medicine, the integration of artificial intelligence (AI) in the medical curriculum is becoming increasingly important. AI technologies such as machine learning and natural language processing can help improve the learning experience for medical students and enhance their understanding of complex medical concepts.

One way AI can be integrated into the medical curriculum is through the use of AI-powered learning platforms. These platforms can provide personalized learning experiences for students, allowing them to access relevant study materials and practice assessments tailored to their individual needs. This can help students to optimize their learning process and improve their overall performance.

Another application of AI in medical education is the use of AI algorithms to analyze and interpret medical images and data. AI-powered tools can help medical students better understand and diagnose various medical conditions by providing accurate and reliable interpretations of medical images. This can significantly enhance their diagnostic skills and decision-making abilities.

Additionally, AI can be used to create virtual patient simulations and scenarios that mimic real-life medical situations. This allows medical students to practice their clinical skills in a safe and controlled environment, without the risk of harming actual patients. Virtual simulations can also provide immediate feedback and guidance to students, helping them to learn from their mistakes and improve their clinical reasoning abilities.

Incorporating AI technologies into the medical curriculum can provide numerous benefits for both students and educators. It can help enhance the quality of medical education by providing personalized learning experiences, improving diagnostic skills, and facilitating practical skills training. By integrating AI into the medical curriculum, we can ensure that future healthcare professionals are well-prepared to meet the challenges of an increasingly complex medical landscape.

Applications of Artificial Intelligence in Medical Education

Artificial intelligence (AI) is revolutionizing the field of medical education, offering new opportunities for students, educators, and healthcare professionals. With the advancements in AI, medical education has become more personalized, interactive, and efficient.

1. Intelligent Tutoring Systems

AI-powered intelligent tutoring systems provide personalized learning experiences to medical students. These systems use algorithms and machine learning techniques to analyze student performance, identify areas of improvement, and develop customized study plans. Adaptive learning platforms can present information in different formats based on individual student preferences, ensuring an engaging and effective learning experience.

2. Virtual Simulation

Virtual simulation is being utilized to enhance medical education by providing immersive, realistic scenarios for students to practice clinical skills and decision-making. AI-powered virtual patients can exhibit realistic symptoms and responses to treatment, allowing students to develop diagnostic and treatment plans. Virtual simulations also offer a safe and controlled environment for students to learn from their mistakes without endangering real patients.

Furthermore, virtual simulations can be combined with AI algorithms to provide real-time feedback and guidance to students, helping them improve their clinical reasoning and decision-making skills.

3. Augmented Reality

Augmented reality (AR) is another application of AI in medical education that enhances the learning experience. With AR, students can interact with virtual objects and anatomical structures, enabling a deeper understanding of complex medical concepts. AR can also be used to visualize patient data, such as medical imaging scans, in a more immersive and intuitive way.

4. Intelligent Assessment Systems

AI can assist in the assessment of medical students’ performance by analyzing their knowledge, skills, and clinical reasoning. Intelligent assessment systems can automatically evaluate and provide feedback on student assessments, saving time for educators and allowing for more frequent and detailed assessments. These systems can also identify knowledge gaps and areas for improvement, helping students tailor their studying strategies accordingly.

In conclusion, AI has the potential to transform medical education by providing personalized learning experiences, realistic simulations, enhanced visualization, and intelligent assessment systems. By harnessing the power of AI, medical education can become more effective, accessible, and engaging for future healthcare professionals.

Improving Student Engagement with Artificial Intelligence

Artificial intelligence is revolutionizing the field of education, and its impact can be especially felt in medical education. With the help of AI, medical students can now engage with the learning materials in a more interactive and personalized manner.

1. Virtual Simulations

AI-powered virtual simulations allow medical students to practice procedures and techniques in a controlled and realistic environment. By immersing themselves in these simulations, students can develop critical skills, gain confidence, and improve their overall understanding of medical concepts.

2. Personalized Learning

AI algorithms can analyze the learning patterns, strengths, and weaknesses of individual students. This allows educators to personalize the learning experience by providing targeted recommendations and resources to enhance the student’s knowledge and understanding of medical topics.

Additionally, AI chatbots can provide instant feedback, answer questions, and offer further explanations for difficult concepts. This personalized approach helps to keep students engaged and motivated throughout their medical education journey.

3. Adaptive Assessments

Traditional assessments tend to be one-size-fits-all, which may not accurately assess each student’s unique learning needs. AI-powered adaptive assessments, on the other hand, can dynamically adjust the difficulty level and content based on the student’s performance.

This personalized approach allows students to be appropriately challenged, ensuring that they stay engaged and motivated. It also provides educators with valuable insights into each student’s progress and areas where they may need additional support.

Overall, artificial intelligence has the potential to revolutionize medical education by improving student engagement. Through virtual simulations, personalized learning, and adaptive assessments, AI can enhance the learning experience and help students become successful medical professionals.

Enhancing Medical Diagnosis with Artificial Intelligence

In recent years, the field of medical education has seen significant advancements with the integration of artificial intelligence (AI). AI has the potential to revolutionize medical diagnosis, improving accuracy and efficiency.

Improved Accuracy: AI algorithms can analyze vast amounts of medical data and identify patterns that human physicians may overlook. This can help in the early detection of diseases and conditions, leading to more accurate diagnoses.

Efficient Data Analysis: AI can process and analyze patient data quickly, saving time for medical professionals. This allows physicians to focus more on patient care rather than spending hours sifting through information.

Personalized Treatment: AI-based algorithms can take into account an individual’s unique biological characteristics and medical history to recommend personalized treatment plans. This can optimize patient outcomes and reduce the risk of adverse effects.

Challenges and Future Directions

While AI holds great promise for medical diagnosis, there are still challenges to overcome. One challenge is ensuring the accuracy and reliability of AI algorithms, as they heavily rely on the quality and diversity of the data they are trained on.

Another challenge is the ethical and legal implications of AI in medical diagnosis. It is crucial to ensure patient privacy and confidentiality while using AI algorithms.

Looking ahead, ongoing research and collaboration between medical professionals and AI experts will be essential in refining and improving AI technologies in medical diagnosis. The potential benefits are immense, and as AI continues to advance, it will play a crucial role in enhancing medical education and patient care.

Personalized Learning with Artificial Intelligence

Artificial intelligence (AI) technology has the potential to revolutionize the field of medical education by offering personalized learning experiences. With AI-powered tools and platforms, educators can create tailored teaching materials and assessments that cater to the unique needs and learning styles of individual students.

Benefits of Personalized Learning

1. Enhanced Engagement: AI-powered learning platforms can provide interactive and immersive experiences, making the learning process more engaging for students.

2. Individualized Pace: AI algorithms can analyze each student’s progress and adjust the learning pace accordingly, ensuring optimal comprehension and retention.

3. Targeted Support: AI can identify areas where individual students are struggling and provide targeted support and remedial materials to improve their understanding.

AI in Medical Education

In medical education, AI-based tools can be used to simulate patient cases, allowing students to practice clinical decision-making in a safe and controlled environment. AI algorithms can also analyze large amounts of medical literature and provide personalized recommendations for further study based on each student’s strengths and weaknesses.

The use of AI in medical education can not only improve students’ learning outcomes but also enable educators to gather valuable data on student performance and identify areas that need improvement. By leveraging AI technology, medical educators can create more effective and efficient learning experiences that better prepare future healthcare professionals.

Artificial Intelligence for Virtual Patient Simulations

The integration of artificial intelligence (AI) into medical education has revolutionized the way students learn and practice their skills. One of the most exciting applications of AI in medical education is the development of virtual patient simulations. These simulations use AI algorithms to replicate real-life patient scenarios, allowing students to practice diagnosing and treating various medical conditions.

Virtual patient simulations offer several benefits over traditional teaching methods. Firstly, they provide a safe and controlled environment for students to develop their clinical skills without the risk of harming real patients. This allows students to make mistakes and learn from them without any consequences.

Another advantage of using AI for virtual patient simulations is the ability to customize scenarios based on individual student needs and skill levels. AI algorithms can adjust the complexity and difficulty of the simulation based on the student’s performance, ensuring they are constantly challenged and engaged.

Improved Learning Outcomes

Studies have shown that incorporating AI into virtual patient simulations can significantly improve learning outcomes. Students who use these simulations tend to retain more information and exhibit higher levels of clinical competence compared to those who rely solely on traditional teaching methods.

AI algorithms can provide real-time feedback and guidance during simulations, pointing out errors and suggesting alternative approaches. This feedback not only helps students correct their mistakes but also enhances their critical thinking and problem-solving skills.

The Future of Medical Education

The integration of artificial intelligence into medical education is still in its early stages, but the potential benefits are immense. As technology continues to advance, AI-powered virtual patient simulations will become even more realistic and sophisticated, providing an even more immersive learning experience for students.

In addition to virtual patient simulations, AI can also be utilized in other areas of medical education, such as personalized learning platforms, intelligent tutors, and adaptive assessment systems. The possibilities are endless, and the future of medical education looks promising with the integration of AI.

Artificial Intelligence in Medical Imaging Education

Artificial intelligence (AI) is revolutionizing the field of medical education, particularly in the area of medical imaging. With the help of AI, medical professionals can now access ppt- based interactive learning tools that enhance their understanding and interpretation of medical images.

Medical imaging plays a crucial role in diagnosing and monitoring various medical conditions. However, effectively interpreting medical images requires extensive knowledge and experience. This is where AI comes in. AI algorithms can analyze vast amounts of medical imaging data and extract relevant information, assisting medical professionals in making accurate diagnoses.

AI-powered medical imaging education platforms utilize machine learning algorithms to provide personalized learning experiences. These platforms can track a learner’s progress, identify areas of weakness, and deliver targeted educational materials and assessments. This adaptive learning approach helps medical students and professionals enhance their skills and improve their diagnostic accuracy.

One key advantage of AI in medical imaging education is its ability to simulate real-world scenarios. Medical students can practice interpreting medical images in a risk-free environment, gaining valuable hands-on experience. This interactive learning approach fosters critical thinking skills and helps students develop their diagnostic intuition.

The integration of AI in medical imaging education also facilitates continuous professional development. AI-powered platforms can keep medical professionals updated with the latest advancements and research in medical imaging. This ensures that their knowledge and skills remain up to date, enabling them to provide high-quality care to their patients.

In conclusion, AI is transforming medical imaging education by providing ppt-based interactive learning tools, personalized learning experiences, simulation of real-world scenarios, and continuous professional development opportunities. With the help of AI, medical professionals can enhance their skills, improve their diagnostic accuracy, and ultimately improve patient outcomes.

Artificial Intelligence for Clinical Decision Support

Artificial intelligence (AI) has the potential to revolutionize medical education and improve clinical decision-making. With the help of AI, medical students and healthcare professionals can gain access to a wealth of knowledge and expertise that can enhance their ability to diagnose and treat patients effectively.

AI-powered clinical decision support systems can analyze vast amounts of medical data, such as patient records, lab results, and research findings, to generate insights and recommendations for healthcare providers. These systems can assist in the identification of potential health conditions, suggest appropriate tests and treatments, and even alert doctors to possible drug interactions or adverse reactions.

By leveraging AI in medical education, educators can create interactive and engaging PowerPoint presentations (PPT) that incorporate real-time clinical case studies and simulations. These presentations can help students develop critical thinking skills and gain practical experience in making accurate diagnoses and treatment plans.

AI can also personalize medical education by adapting to individual learner needs. By analyzing the learner’s performance and identifying knowledge gaps, AI algorithms can provide targeted feedback and recommend additional resources or learning materials. This personalized approach to medical education can optimize the learning process and ensure that students acquire the necessary skills and knowledge to become competent healthcare professionals.

In conclusion, the integration of artificial intelligence in medical education has the potential to revolutionize the way healthcare professionals learn and improve clinical decision-making. By leveraging AI for clinical decision support, medical students and healthcare professionals can access a wealth of knowledge and expertise, while AI-powered educational tools can enhance the learning experience and maximize learning outcomes.

Tracking and Analyzing Student Performance with Artificial Intelligence

Artificial intelligence (AI) is revolutionizing the education sector, and one of its significant contributions is in tracking and analyzing student performance. With the help of AI, educators can gather valuable insights into how students are progressing, identify areas of improvement, and make data-driven decisions to enhance their learning experience.

AI-powered education platforms can track students’ performance in real-time, analyzing their engagement levels, completion rates, and comprehension of the material. Through sophisticated algorithms, AI can identify patterns and trends in students’ progress over time, allowing educators to have a comprehensive understanding of their strengths and weaknesses.

Furthermore, AI can provide personalized feedback to students based on their performance. By analyzing their answers and interactions with educational materials, AI algorithms can identify misunderstandings, offer clarifications, and suggest additional resources to support students’ learning process. This personalized feedback improves students’ understanding and helps them bridge any knowledge gaps.

AI also plays a crucial role in detecting early signs of struggling students. By analyzing students’ performance data, AI algorithms can identify red flags, such as declining grades or a lack of engagement, indicating that a student may be struggling academically. With this information, educators can intervene early and provide timely support to keep students on track.

Moreover, AI can assist educators in evaluating the effectiveness of their teaching strategies. By analyzing student performance data at a larger scale, AI algorithms can help identify which teaching methods are most successful and which require adjustment. This data-driven approach allows educators to continuously improve their teaching methods and adapt to the individual needs of their students.

In conclusion, the use of artificial intelligence in tracking and analyzing student performance is transforming the field of education. AI enables educators to gain valuable insights into student progress, provide personalized feedback, detect struggling students early, and evaluate teaching methods. By harnessing the power of AI, we can create more effective and efficient learning environments to support the academic success of every student.

Ethical Considerations in the Use of Artificial Intelligence in Medical Education

The introduction of artificial intelligence (AI) technology in medical education has the potential to greatly enhance the learning experience for future healthcare professionals. However, it also raises important ethical considerations that must be carefully addressed.

Firstly, the use of AI in medical education must prioritize patient safety and privacy. As AI algorithms analyze and process large amounts of sensitive medical data, it is essential to ensure that patient information is protected and used only for educational purposes. Informed consent and strict data security measures should be in place to maintain confidentiality and prevent any misuse of patient data.

Another ethical concern is the potential bias in AI algorithms. AI systems are trained using large datasets, which can inadvertently include biased or discriminatory information. This can lead to biased recommendations or diagnoses, negatively impacting patient care. It is crucial to regularly audit and review AI algorithms to identify and mitigate any biases or discriminatory patterns.

Transparency and explainability are also important ethical considerations in AI-driven medical education. Healthcare professionals and students using AI systems should have a clear understanding of how the technology makes decisions and recommendations. The black-box nature of some AI models can raise concerns about accountability and trust. Clear guidelines should be established to ensure transparency and allow for meaningful human oversight of AI systems.

The potential for job displacement is another ethical consideration in the use of AI in medical education. While AI technology can automate certain tasks and improve efficiency, it may also lead to the loss of certain healthcare jobs. It is crucial to acknowledge and address the potential impact on healthcare professionals, providing necessary training and support to adapt to the changing landscape of healthcare education.

Lastly, there is a need to address the digital divide and ensure equitable access to AI-driven medical education. Not all healthcare institutions or regions may have the same resources or capabilities to implement AI technology. Efforts should be made to bridge this gap and ensure that all healthcare professionals have equal opportunities to benefit from AI-driven education.

In conclusion, the integration of AI in medical education brings immense possibilities for improving learning outcomes. However, it is imperative to consider and address the ethical implications associated with the use of artificial intelligence. By prioritizing patient safety and privacy, mitigating bias, ensuring transparency, addressing job displacement, and promoting equitable access, AI technology can be effectively utilized in medical education while adhering to ethical principles.

Preparing Students for the Future of Medicine with Artificial Intelligence

As advancements in technology continue to reshape various industries, it is crucial for the field of medicine to keep up with the latest innovations. One such innovation that holds promise for revolutionizing medical education is artificial intelligence (AI). By incorporating AI into medical education, students can be better prepared for the future of medicine and equipped with the skills they need to thrive in a rapidly evolving healthcare landscape.

Fostering a Collaborative Learning Environment

Medical education has traditionally relied on didactic teaching methods, with students passively receiving information through lectures and textbooks. However, AI-powered tools, such as interactive PowerPoint presentations (PPTs), can transform medical education by creating a more engaging and interactive learning environment. PPTs enhanced with AI algorithms can generate personalized quizzes, provide real-time feedback, and simulate medical scenarios, allowing students to actively participate in their learning process.

Moreover, AI technology can facilitate collaborative learning experiences among students. Through online platforms and virtual classrooms, students can interact with their peers and participate in discussions and problem-solving activities. Such collaborative approaches prepare students for the future of medicine, as teamwork and effective communication will be essential in providing quality patient care in an AI-enabled healthcare system.

Enhancing Diagnostic Skills

Artificial intelligence has already proven to be a valuable tool in diagnostic medicine. AI algorithms can analyze medical data, such as imaging scans and patient histories, to detect patterns and make accurate diagnoses. By incorporating AI-powered diagnostic tools into their training, students can develop their diagnostic skills and become more adept at interpreting complex medical data.

Additionally, AI can provide students with access to vast databases and medical literature, allowing them to stay up-to-date with the latest research and treatment guidelines. This access to knowledge can help students develop a deep understanding of medical concepts and foster a culture of lifelong learning.

In conclusion, incorporating artificial intelligence into medical education has the potential to prepare students for the future of medicine by fostering a collaborative learning environment, enhancing diagnostic skills, and providing access to the latest medical knowledge. By embracing AI technology, medical schools can equip future healthcare professionals with the tools they need to meet the challenges of an ever-changing healthcare landscape.

Current Limitations and Future Directions

Despite the numerous advancements in artificial intelligence and its potential impact on medical education, there are still some limitations that need to be addressed in order to fully harness its benefits.

Limited Data Availability

One of the main challenges in implementing AI technology in medical education is the limited availability of high-quality data. AI algorithms require large datasets in order to train and learn effectively. However, collecting and curating such datasets can be time-consuming and resource-intensive. Moreover, there may be privacy concerns related to the use of patient data in training AI models.

Lack of Standardization

Another limitation is the lack of standardization in medical education. AI algorithms often rely on structured data and common formats to process information. However, medical education materials, such as textbooks and lectures, come in various formats and styles. This lack of standardization makes it difficult for AI systems to accurately analyze and extract information from different sources.

To overcome these limitations and further enhance the use of artificial intelligence in medical education, several future directions can be considered:

• Development of comprehensive and standardized medical datasets specifically designed for AI training purposes.
• Implementation of secure and privacy-preserving protocols for collecting and sharing patient data for AI research and development.
• Collaboration between medical educators, AI researchers, and software developers to create standardized formats and structures for medical education materials.
• Integration of AI technology into existing medical education platforms and systems to provide personalized learning experiences for students.
• Continued research and development in natural language processing and machine learning algorithms to improve the accuracy and efficiency of AI systems in medical education.

By addressing these current limitations and exploring these future directions, the field of medical education can fully leverage the potential of artificial intelligence to enhance learning outcomes and improve patient care.

Case Studies: Successful Implementation of Artificial Intelligence in Medical Education

In recent years, the integration of artificial intelligence (AI) technologies in medical education has shown promising results. These case studies highlight successful implementations of AI in medical education, demonstrating its potential to improve the learning experience and outcomes for future healthcare professionals.

1. Virtual Patient Simulations

Virtual patient simulations are an innovative use of AI in medical education. These simulations provide students with realistic scenarios and patients to diagnose and treat virtually. AI algorithms are used to mimic the behavior of these virtual patients, allowing students to practice their clinical reasoning and decision-making skills in a safe and controlled environment. This application of AI has been shown to enhance critical thinking and diagnostic accuracy among medical students.

2. Personalized Learning Pathways

AI algorithms can analyze large volumes of data and provide personalized learning pathways tailored to each student’s needs. By assessing students’ knowledge gaps and learning styles, AI-powered platforms can deliver targeted educational content and resources to optimize learning. This personalized approach ensures that students receive the specific guidance and support they require, facilitating their understanding and retention of complex medical concepts.

Overall, the successful implementation of AI in medical education has the potential to revolutionize the way healthcare professionals are trained. By leveraging AI technologies, medical education can be made more interactive, engaging, and tailored to individual student needs. As technology continues to advance, AI is expected to play an even greater role in transforming medical education and improving the quality of healthcare globally.

Collaboration between Medical Educators and Artificial Intelligence Experts

Artificial intelligence (AI) has the potential to revolutionize medical education and improve the way healthcare professionals are trained. By harnessing the power of AI, medical educators can enhance the learning experience for students and provide them with valuable tools and resources.

Collaboration between medical educators and AI experts is crucial for the development and implementation of effective educational tools. AI experts can contribute their knowledge and expertise in data analysis, machine learning, and natural language processing to create intelligent systems that can assist in medical education.

AI-powered tools can provide personalized learning experiences by adapting to the individual needs and learning styles of each student. These tools can analyze data from various sources, such as textbooks, research articles, and clinical cases, to generate personalized recommendations and assessments.

Furthermore, AI can help medical educators in curriculum design and assessment. By analyzing large amounts of data from medical textbooks, research articles, and clinical guidelines, AI can identify knowledge gaps and suggest areas of improvement for medical curricula. AI can also assist in the assessment of student performance by analyzing their answers to questions and providing feedback in real-time.

While AI is a powerful tool, it is essential for medical educators to maintain an active role in the development and use of AI-powered educational tools. Medical educators can provide valuable insights and expertise in the design of educational content and ensure its relevance and accuracy.

In conclusion, collaboration between medical educators and AI experts is essential for leveraging the full potential of artificial intelligence in medical education. By working together, they can develop innovative tools and strategies that enhance the learning experience for medical students and improve the quality of healthcare education.

Training Medical Educators in Artificial Intelligence

As the field of medicine continues to evolve, it is crucial for medical educators to stay updated with the latest advancements in technology. One area that holds great potential for improving medical education is artificial intelligence (AI).

AI has the ability to process and analyze large amounts of data, which can be invaluable in medical research, diagnosis, and treatment. It can also be used to enhance the learning experience of medical students, allowing them to practice skills and scenarios in a virtual environment.

However, in order to effectively incorporate AI into medical education, it is essential for medical educators to have a solid understanding of this technology. They need to be equipped with the knowledge and skills to not only use AI tools and applications but also to teach students how to effectively utilize AI in their medical practice.

Training medical educators in artificial intelligence can be achieved through various means. Workshops and seminars can provide educators with hands-on experience and practical knowledge about AI applications in medicine. Online courses and certifications can also be valuable resources to enhance educators’ understanding of AI and its applications.

Moreover, it is important for medical educators to collaborate with experts in the field of AI to develop curricula that integrate AI concepts and techniques into medical education programs. This collaboration can ensure that educators are able to effectively teach AI to medical students and provide them with the necessary knowledge and skills to leverage AI in their future medical practice.

By training medical educators in artificial intelligence, we can ensure that the next generation of healthcare professionals is equipped with the necessary skills to harness the power of AI in delivering quality medical care. With the rapid advancements in AI technology, it is imperative for medical education to keep pace and prepare future medical professionals for a technology-driven healthcare landscape.

Addressing Privacy and Security Concerns in Artificial Intelligence in Medical Education

As artificial intelligence (AI) continues to revolutionize various industries, including education, it is important to address the privacy and security concerns that come with its integration into medical education.

AI-powered education tools, such as PowerPoint presentations (PPTs), have the potential to enhance the learning experience by providing personalized and interactive content. However, the collection and analysis of sensitive medical data raise privacy concerns. Medical students and educators need to have confidence that their personal information is protected and that the AI systems they use are secure.

One way to address these concerns is through clear and transparent data governance policies. Educational institutions should establish guidelines on how student and faculty data is collected, stored, and used. These policies should also include provisions for obtaining informed consent from individuals involved in the AI-enabled educational processes.

Another important aspect is data security. AI systems used in medical education should be designed with robust security measures to protect against unauthorized access or data breaches. This includes encrypting sensitive data, implementing firewalls, and regularly updating the AI software to address any security vulnerabilities that may arise.

Additionally, ethical considerations should be taken into account when using AI in medical education. AI algorithms should be developed and trained with unbiased and diverse data to prevent any potential discrimination or biases in the educational content. Transparency in the use of AI systems is crucial, as it helps to build trust and instill confidence in the learners and educators.

In conclusion, while the integration of AI into medical education holds great potential for improving the learning experience, it is important to address privacy and security concerns. Clear data governance policies, robust security measures, and ethical considerations can help ensure that AI is used responsibly and ethically in medical education.

Cost-Effectiveness of Implementing Artificial Intelligence in Medical Education

Artificial intelligence (AI) has the potential to revolutionize medical education, offering new opportunities for students to learn and practice their skills in a more efficient and effective manner. While there is an initial investment required to implement AI technologies in medical education, the long-term benefits far outweigh the costs.

One of the main advantages of using AI in medical education is its ability to provide personalized learning experiences. AI-powered platforms can analyze the learning patterns and preferences of individual students, tailoring the educational content to their specific needs. This personalized approach can help students learn at their own pace, ensuring that they fully understand the material before moving on to the next topic.

Additionally, AI can provide real-time feedback and assessment, allowing students to track their progress and identify areas where they need to improve. This instant feedback enables students to address their weaknesses promptly, leading to faster and more effective learning outcomes.

Another significant benefit of implementing AI in medical education is the ability to simulate medical scenarios and surgeries. Virtual reality (VR) and augmented reality (AR) technologies can create immersive learning experiences, where students can practice medical procedures in a safe and controlled environment. This type of hands-on training can significantly enhance students’ skills and confidence, ultimately leading to better patient care.

Furthermore, AI can assist in knowledge retrieval and retention. AI-powered platforms can provide students with quick access to vast amounts of medical information, simplifying the process of studying and researching complex topics. Students can rely on AI algorithms to filter and present relevant information, saving them time and effort.

In terms of cost-effectiveness, implementing AI in medical education can lead to long-term savings. Traditional teaching methods often require significant resources, such as textbooks, lecture halls, and physical models. AI technologies can replace or supplement these resources, reducing the overall costs associated with medical education.

In conclusion, the implementation of artificial intelligence in medical education can bring numerous benefits, including personalized learning experiences, real-time feedback, hands-on training, and efficient knowledge retrieval. While there may be an initial investment required, the long-term cost savings and improved learning outcomes make AI a cost-effective solution for medical education.

Impact of Artificial Intelligence on Medical Education Accreditation

Artificial intelligence (AI) is revolutionizing many industries, and the field of medical education is no exception. AI-powered tools and technologies can have a significant impact on the accreditation process for medical education programs, improving efficiency, accuracy, and objectivity.

Enhanced Evaluation Process

AI can assist in evaluating medical education programs by analyzing large amounts of data and providing valuable insights. By using AI algorithms, accreditation boards can analyze student performance, curriculum effectiveness, and faculty qualifications in a more comprehensive and objective manner.

These AI-powered evaluation systems can identify patterns and trends that would be difficult for humans to detect, allowing for more targeted interventions and improvements. By automating certain tasks, such as analyzing student assessments and feedback, AI can reduce the administrative burden on accreditation boards, allowing them to focus on more strategic aspects of the accreditation process.

Standardized Assessments

AI can also play a role in standardizing assessments in medical education. By leveraging AI technologies, medical schools can develop standardized exams that adapt to the individual learning needs of students. These adaptive assessments can provide personalized feedback and identify areas where students need additional support or remediation.

Furthermore, AI can analyze the results of these assessments to identify trends and gaps in knowledge, enabling medical schools to continuously improve their curricula and teaching methods. Standardized assessments powered by AI can also help ensure that all students are evaluated on the same criteria, promoting fairness and equity in medical education accreditation.

The impact of artificial intelligence on medical education accreditation is substantial. AI-powered tools can enhance the evaluation process, providing valuable insights and allowing for targeted interventions. Additionally, AI can aid in standardizing assessments, promoting fairness and continuous improvement. As AI continues to evolve, its role in medical education accreditation will likely expand, leading to even more advancements in the field.

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Question-answer:

How can artificial intelligence improve medical education.

Artificial intelligence can improve medical education by providing personalized and adaptive learning experiences for students. It can analyze the learning needs and preferences of individual students and deliver content tailored to their specific needs. This can help students learn more efficiently and effectively.

Will artificial intelligence replace human teachers in medical education?

No, artificial intelligence will not replace human teachers in medical education. Instead, it will complement the role of human teachers by providing them with data and insights that can help them better understand the learning needs of their students. AI can also automate certain administrative tasks, allowing teachers to focus more on teaching and mentoring.

Which areas of medical education can benefit the most from artificial intelligence?

Several areas of medical education can benefit from artificial intelligence. One area is the assessment of student performance. AI can analyze student data and provide feedback on their strengths and areas for improvement. Another area is the creation of personalized learning plans. AI can analyze the learning needs of individual students and recommend specific resources and activities. Additionally, AI can assist in medical research by analyzing large amounts of data and identifying patterns and correlations.

What are the challenges of implementing artificial intelligence in medical education?

There are several challenges of implementing artificial intelligence in medical education. One challenge is the need for high-quality data. AI algorithms require large amounts of accurate and reliable data to produce meaningful insights. Another challenge is the ethical and privacy considerations. AI systems must adhere to strict privacy and confidentiality standards to protect patient and student data. Finally, there is a need for training and education of teachers and students to effectively use AI tools and technologies.

How can artificial intelligence enhance the learning experience of medical students?

Artificial intelligence can enhance the learning experience of medical students in several ways. It can provide personalized and adaptive feedback to help students track their progress and identify areas for improvement. AI can also recommend relevant study materials and resources based on the individual learning needs of students. Additionally, AI can assist in virtual simulations and realistic case studies, allowing students to practice their skills in a safe and controlled environment.

Artificial intelligence can improve medical education by providing personalized learning experiences, facilitating virtual simulations, and offering real-time feedback to students. This technology can adapt to individual learning styles and needs, enhancing the overall effectiveness of medical education.

What are the potential benefits of using PowerPoint presentations in medical education?

PowerPoint presentations can enhance medical education by organizing and presenting complex information in a visually appealing and easily comprehensible format. They can improve knowledge retention, facilitate interactive discussions, and serve as a valuable reference tool for students.

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How AI improves physician and nurse collaboration

A new artificial intelligence model helps physicians and nurses work together at Stanford Hospital to boost patient care.

April 15, 2024 - By Hanae Armitage

An artificial intelligence model in use at Stanford Hospital helps physicians and nurses know when a patient may be in decline, so they can act quickly to keep them out of the intensive care unit. vachcameraman  -  stock.adobe.com

With large language models that take notes during patient visits and algorithms that identify disease, artificial intelligence has begun to prove its worth as an assistant for physicians. But a new study from Stanford Medicine shows the potential of AI as a facilitator — one that helps doctors and nurses connect to achieve more efficient, effective patient care.

The study , which published in JAMA Internal Medicine last month, describes an AI-based model in use at Stanford Hospital that predicts when a patient is declining and flags the patient’s physicians and nurses. Ron Li , MD, a clinical associate professor of medicine and medical informatics director for digital health who is the senior author on the study, said the alert system helps clinicians connect more efficiently and effectively as well as intervene to prevent patients from deteriorating and landing in the intensive care unit.

Li, who worked with informatics postdoctoral scholar and lead author Robert Gallo , MD, on the evaluation, discussed their team’s approach to harnessing the algorithm and how it fosters clinician connection in a ceaselessly buzzing hospital environment. Lisa Shieh , MD, PhD, clinical professor of medicine; Margaret Smith , executive director of the Healthcare AI Applied Research Team , operations for primary care and population health; and Jerri Westphal, nursing informatics manager, also helped lead the study and the implementation of the AI system.

What is a deterioration model and how does AI fit in?

The algorithm is a prediction model that pulls data — such as vital signs, information from electronic health records and lab results — in near-real time to predict whether a patient in the hospital is about to suffer a health decline. Physicians aren’t able to monitor all of these data points for every patient all of the time, so the model runs in the background, looking at these values about every 15 minutes. It then uses artificial intelligence to calculate a risk score on the probability the patient is going to deteriorate, and if the patient seems like they might be declining, the model sends an alert to the care team.

What’s the benefit of having such a model run in a hospital?

The big question I want to answer is, “How do we use AI to build a more resilient health system in high-stakes situations?” There are many ways to do that, but one core characteristic for a resilient system is strong communication channels. This model is powered by AI, but the action it triggers, the intervention, is basically a conversation that otherwise may not have happened.

Nurses and physicians have conversations and handoffs when they change shifts, but it’s difficult to standardize these communication channels due to busy schedules and other hospital dynamics. The algorithm can help standardize it and draw clinicians’ attention to a patient who may need additional care. Once the alert comes into the nurse and physician simultaneously, it initiates a conversation about what the patient needs to ensure they don’t decline to the point of requiring a transfer to the ICU.

Tell me about how your team implemented and evaluated the model.

We integrated this model, which we did not create, into our workflow, but with a few tweaks. Originally, it sent an alert when the patient was already deteriorating, which we didn’t find very helpful. We adjusted the model to focus on predicting ICU transfers and other indicators of health decline.

We wanted to ensure the nursing team was heavily involved and felt empowered to initiate conversations with physicians about adjusting a patient’s care. When we evaluated the tool, which we had running for almost 10,000 patients, we saw a significant improvement in clinical outcomes — a 10.4% decrease in deterioration events, which we defined as transfers to the ICU, rapid response team events, or codes — among a subset of 963 patients with risk scores within a “regression discontinuity window,” which basically means they’re at the cusp of being high risk. These are patients whose clinical trajectory may not be as obvious to the medical team. For that group of patients, this model was especially helpful for encouraging physicians and nurses to collaborate to determine which patients need extra tending.

How have nurses and physicians responded to the integration of this new model?

The model is far from perfect. The reactions have overall been positive, but there is concern about alert fatigue, since not all alerts are flagging a real decline. When the model was validated on data from patients prior to implementation, we calculated that about 20% of patients flagged by the model did end up experiencing a deterioration event within six to 18 hours. At this point, even though it’s not a completely accurate model, it’s accurate enough to warrant a conversation. It shows that the algorithm doesn’t have to be perfect for it to be effective.

With that said, we want to improve the accuracy; you need to do that to improve trust. That’s what we’re working on now.

For more news about responsible AI in health and medicine,  sign up  for the RAISE Health newsletter.

Register  for the RAISE Health Symposium on May 14.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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• Published: 18 April 2024

Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

• James Shaw 1 , 13 ,
• Joseph Ali 2 , 3 ,
• Caesar A. Atuire 4 , 5 ,
• Phaik Yeong Cheah 6 ,
• Armando Guio Español 7 ,
• Judy Wawira Gichoya 8 ,
• Adrienne Hunt 9 ,
• Daudi Jjingo 10 ,
• Katherine Littler 9 ,
• Daniela Paolotti 11 &
• Effy Vayena 12  

BMC Medical Ethics volume  25 , Article number:  46 ( 2024 ) Cite this article

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Peer Review reports

Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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Berman Institute of Bioethics, Johns Hopkins University, Baltimore, MD, USA

Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

Department of Philosophy and Classics, University of Ghana, Legon-Accra, Ghana

Caesar A. Atuire

Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

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Berkman Klein Center, Harvard University, Bogotá, Colombia

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Department of Radiology and Informatics, Emory University School of Medicine, Atlanta, GA, USA

Judy Wawira Gichoya

Health Ethics & Governance Unit, Research for Health Department, Science Division, World Health Organization, Geneva, Switzerland

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JS led the writing, contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. CA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. PYC contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AE contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JWG contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AH contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DJ contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. KL contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DP contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. EV contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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DOI : https://doi.org/10.1186/s12910-024-01044-w

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AI Index: State of AI in 13 Charts

In the new report, foundation models dominate, benchmarks fall, prices skyrocket, and on the global stage, the U.S. overshadows.

This year’s AI Index — a 500-page report tracking 2023’s worldwide trends in AI — is out.

The index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. This year’s report covers the rise of multimodal foundation models, major cash investments into generative AI, new performance benchmarks, shifting global opinions, and new major regulations.

Don’t have an afternoon to pore through the findings? Check out the high level here.

A Move Toward Open-Sourced

This past year, organizations released 149 foundation models, more than double the number released in 2022. Of these newly released models, 65.7% were open-source (meaning they can be freely used and modified by anyone), compared with only 44.4% in 2022 and 33.3% in 2021.

But At a Cost of Performance?

Closed-source models still outperform their open-sourced counterparts. On 10 selected benchmarks, closed models achieved a median performance advantage of 24.2%, with differences ranging from as little as 4.0% on mathematical tasks like GSM8K to as much as 317.7% on agentic tasks like AgentBench.

Biggest Players

Industry dominates AI, especially in building and releasing foundation models. This past year Google edged out other industry players in releasing the most models, including Gemini and RT-2. In fact, since 2019, Google has led in releasing the most foundation models, with a total of 40, followed by OpenAI with 20. Academia trails industry: This past year, UC Berkeley released three models and Stanford two.

Industry Dwarfs All

If you needed more striking evidence that corporate AI is the only player in the room right now, this should do it. In 2023, industry accounted for 72% of all new foundation models.

Prices Skyrocket

One of the reasons academia and government have been edged out of the AI race: the exponential increase in cost of training these giant models. Google’s Gemini Ultra cost an estimated $191 million worth of compute to train, while OpenAI’s GPT-4 cost an estimated $78 million. In comparison, in 2017, the original Transformer model, which introduced the architecture that underpins virtually every modern LLM, cost around $900.

What AI Race?

At least in terms of notable machine learning models, the United States vastly outpaced other countries in 2023, developing a total of 61 models in 2023. Since 2019, the U.S. has consistently led in originating the majority of notable models, followed by China and the UK.

Move Over, Human

As of 2023, AI has hit human-level performance on many significant AI benchmarks, from those testing reading comprehension to visual reasoning. Still, it falls just short on some benchmarks like competition-level math. Because AI has been blasting past so many standard benchmarks, AI scholars have had to create new and more difficult challenges. This year’s index also tracked several of these new benchmarks, including those for tasks in coding, advanced reasoning, and agentic behavior.

Private Investment Drops (But We See You, GenAI)

While AI private investment has steadily dropped since 2021, generative AI is gaining steam. In 2023, the sector attracted $25.2 billion, nearly ninefold the investment of 2022 and about 30 times the amount from 2019 (call it the ChatGPT effect). Generative AI accounted for over a quarter of all AI-related private investments in 2023.

U.S. Wins $$ Race

And again, in 2023 the United States dominates in AI private investment. In 2023, the $67.2 billion invested in the U.S. was roughly 8.7 times greater than the amount invested in the next highest country, China, and 17.8 times the amount invested in the United Kingdom. That lineup looks the same when zooming out: Cumulatively since 2013, the United States leads investments at $335.2 billion, followed by China with $103.7 billion, and the United Kingdom at $22.3 billion.

Where is Corporate Adoption?

More companies are implementing AI in some part of their business: In surveys, 55% of organizations said they were using AI in 2023, up from 50% in 2022 and 20% in 2017. Businesses report using AI to automate contact centers, personalize content, and acquire new customers. 

Younger and Wealthier People Worry About Jobs

Globally, most people expect AI to change their jobs, and more than a third expect AI to replace them. Younger generations — Gen Z and millennials — anticipate more substantial effects from AI compared with older generations like Gen X and baby boomers. Specifically, 66% of Gen Z compared with 46% of boomer respondents believe AI will significantly affect their current jobs. Meanwhile, individuals with higher incomes, more education, and decision-making roles foresee AI having a great impact on their employment.

While the Commonwealth Worries About AI Products

When asked in a survey about whether AI products and services make you nervous, 69% of Aussies and 65% of Brits said yes. Japan is the least worried about their AI products at 23%.  

Regulation Rallies

More American regulatory agencies are passing regulations to protect citizens and govern the use of AI tools and data. For example, the Copyright Office and the Library of Congress passed copyright registration guidance concerning works that contained material generated by AI, while the Securities and Exchange Commission developed a cybersecurity risk management strategy, governance, and incident disclosure plan. The agencies to pass the most regulation were the Executive Office of the President and the Commerce Department. 

The AI Index was first created to track AI development. The index collaborates with such organizations as LinkedIn, Quid, McKinsey, Studyportals, the Schwartz Reisman Institute, and the International Federation of Robotics to gather the most current research and feature important insights on the AI ecosystem. 

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How A.I. Tools Could Change India’s Elections

Avatars are addressing voters by name, in whichever of India’s many languages they speak. Experts see potential for misuse in a country already rife with disinformation.

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By Suhasini Raj

Reporting from Pushkar, India

For a glimpse of where artificial intelligence is headed in election campaigns, look to India, the world’s largest democracy, as it starts heading to the polls on Friday.

An A.I.-generated version of Prime Minister Narendra Modi that has been shared on WhatsApp shows the possibilities for hyperpersonalized outreach in a country with nearly a billion voters. In the video — a demo clip whose source is unclear — Mr. Modi’s avatar addresses a series of voters directly, by name.

However, it is not perfect. Mr. Modi appears to wear two different pairs of glasses, and some parts of the video are pixelated.

Down the ladder, workers in Mr. Modi’s party are sending videos by WhatsApp in which their own A.I. avatars deliver personal messages to specific voters about the government benefits they have received and ask for their vote.

Those video messages can be automatically generated in whichever of India’s dozens of languages the voter speaks. So can phone messages by A.I.-powered chatbots that call constituents in the voices of political leaders and seek their support.

Such outreach requires a fraction of the time and money spent on traditional campaigning, and it has the potential to become an essential instrument in elections. But as the technology races onto the political scene, there are few guardrails to prevent misuse.

Chatbots and personalized videos may seem more or less harmless. Experts worry, however, that voters will have an increasingly difficult time distinguishing between real and synthetic messages as the technology advances and spreads.

“It’ll be the Wild West and an unregulated A.I. space this year,” said Prateek Waghre, the executive director of the Internet Freedom Foundation, a digital rights group based in New Delhi. The technology, he added, is entering a media landscape already polluted with misinformation.

Around the world, elections have become a testing ground for the A.I. boom. The tools have been used to turn an Argentine presidential candidate into Indiana Jones and a Ghostbuster. During the New Hampshire primary, voters received robocall messages urging them not to vote, in a voice that was most likely artificially generated to sound like President Biden’s.

And in India, Mr. Modi’s Bharatiya Janata Party, or B.J.P., and the opposition Indian National Congress party have accused each other of spreading election-related deepfake content online.

One outpost on this new Indian frontier is in the western desert state of Rajasthan. On the ground floor of a residential building on a dusty back lane, a 31-year-old college dropout, Divyendra Singh Jadoun, operates an A.I. start-up, The Indian Deepfaker.

His team of nine people has been making commercials with A.I.-generated avatars of Bollywood actors and actresses. But earlier this year, political parties and politicians began asking him to do for them what he had done for celebrities. Of the 200 requests, Mr. Jadoun said, he took on 14.

Among those getting the A.I. treatment is Shakti Singh Rathore, a 33-year-old B.J.P. member. His job this election season is to tell as many people as possible about Mr. Modi’s programs and policies. So he decided to create a replica of himself.

“A.I. is wonderful and the way forward,” Mr. Rathore said as he settled in front of a video camera at the office of The Indian Deepfaker, preparing to become digitally incarnated. “How else could I reach the beneficiaries of Mr. Modi’s programs in such large numbers and in so short a period of time?”

As Mr. Rathore adjusted a saffron scarf with the party’s logo that hung around his neck, Mr. Jadoun instructed him, “Just look into the camera and talk as if the person is sitting right in front of you.”

With about five minutes’ worth of material, including an audio recording and profile shots, Mr. Jadoun went to work. He said he uses open-source A.I. systems and builds upon them with his own code.

First, Mr. Rathore’s face was isolated from each frame of the recording.

Then data was collected from his facial features, including the size of his face and lips, as well as his gaze.

Mr. Jadoun said the data set was then fed into A.I. models that learn to predict facial patterns.

“You need to keep running it through the program and fine-tuning the face until you get the best face possible,” he said.

A “cloning algorithm” also analyzed the audio recording, learning the voice’s cadence and intonations. Mr. Jadoun said it often takes six to eight hours of tweaking to perfect the face and for the lips to sync with the words. The rest is largely automated.

In one demo, it took about four minutes to create around 20 personalized greeting videos.

Mr. Jadoun said his team could produce up to 10,000 videos a day. For larger jobs on deadline, it will rent graphics processing units.

Generative A.I. can also remove language barriers, which is especially helpful in a linguistically diverse country. Mr. Rathore’s avatar can be programmed to speak regional languages to reach the remotest corners of India.

Political parties are not only texting constituents video messages but also using cloned voices to call people directly, all powered by chatbots like ChatGPT.

In the past, when a party representative would call voters, they would hang up, Mr. Rathore said. “But now, when a local leader utters a voter’s name, it immediately catches their attention.”

During the conversation, the chatbot asks about local government programs that offer free electricity or funding for start-ups. Mr. Jadoun said the calls were recorded and transcribed for quality control and A.I. training.

Mr. Rathore said he had spent around $24,000 of his own money to reach about 1.2 million people through his video messages and phone calls and to receive information about who didn’t answer. He called it an investment in his future with the B.J.P.

Nikhil Pahwa, the editor of MediaNama, which covers digital media in India, said the personalized messages could be particularly powerful among Indians.

“India is a country where people love to take photos with celebrity impersonators,” he said. “So if they receive a call from, say, the prime minister, and he speaks as if he knows them, where they live and what their issues are, they would actually be thrilled about it.”

Mr. Waghre of the Internet Freedom Foundation questions whether A.I. content is persuasive enough to affect this year’s election. But he said the long-term effects could be problematic. “Once you normalize this in people’s information diet, what happens six months later when there are deceptive videos?” he said.

Mr. Modi himself has discussed adding disclaimers to A.I.-generated content so people are not being “ misguided .” Mr. Jadoun and representatives of two other A.I. start-ups in India created what they call an “ A.I. coalition manifesto ,” pledging to protect data privacy and uphold election integrity. For instance, Indian Deepfaker videos are labeled “A.I. generated,” and its chatbots announce that they are A.I.-generated voices, Mr. Jadoun said.

Narendra Singh Bhati, 28, the owner of resorts in Rajasthan, received an A.I.-generated call from Mr. Rathore this week. Mr. Bhati said he was impressed with its personalization.

He said he had not realized that the call was A.I.-generated, although the script made that clear. “I even said goodbye to Mr. Rathore” at the end, Mr. Bhati said.

Suhasini Raj is a reporter based in New Delhi who has covered India for The Times since 2014. More about Suhasini Raj