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• Research Guides
• University Libraries

AI-Based Literature Review Tools

• Dialogues: Insightful Facts
• How to Craft Prompts
• Plugins / Extensions for AI-powered Searches
• Cite ChatGPT in APA / MLA
• AI and Plagiarism
• ChatGPT & Higher Education
• Author Profile

Selected AI-Based Literature Review Tools

Updates: See news or release of AI (Beta) across various academic research databases including Web of Science , Scopus , Ebsco , ProQues t, OVID , Dimensions , JStor , Westlaw , and LexisNexis . ********* ********** ********** ********** **********   

Disclaimer: TAMU libraries do not have subscription access to the AI-powered tools listed below the divider line. The guide serves solely as an informational resource. It is recommended that you assess these tools and their usage methodologies independently. ------------------------------------------------------------------------------------------------------------------------------------------------------------- SEMANTIC SCHOLAR

• SCIENTIFIC LITERATURE SEARCH ENGINE - finding semantically similar research papers.
• " A free, AI-powered research tool for scientific literature."  <https://www.semanticscholar.org/>. But login is required in order to use all functions.
• Over 200 millions of papers from all fields of science, the data of which has also served as a wellspring for the development of other AI-driven tools.

The 4000+ results can be sorted by Fields of Study, Date Range, Author, Journals & Conferences

Save the papers in your Library folder. The Research Feeds will recommend similar papers based on the items saved.

Example - SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality Total Citations: 22,438   [Note: these numbers were gathered when this guide was created] Highly Influential Citations 2,001 Background Citations 6,109 Methods Citations 3,273  Results Citations 385

TLDRs (Too Long; Didn't Read) Try this example . Press the pen icon to reveal the highlighted key points . TLDRs "are super-short summaries of the main objective and results of a scientific paper generated using expert background knowledge and the latest GPT-3 style NLP techniques. This new feature is available in beta for nearly 60 million papers in computer science, biology, and medicine..." < https://www.semanticscholar.org/product/tldr>

• AI-POWERED RESEARCH ASSISTANT - finding papers, filtering study types, automating research flow, brainstorming, summarizing and more.
• " Elicit is a research assistant using language models like GPT-3 to automate parts of researchers’ workflows. Currently, the main workflow in Elicit is Literature Review. If you ask a question, Elicit will show relevant papers and summaries of key information about those papers in an easy-to-use table."   <https://elicit.org/faq#what-is-elicit.>; Find answers from 175 million papers. FAQS
• Example - How do mental health interventions vary by age group?    /   Fish oil and depression Results: [Login required] (1) Summary of top 4 papers > Paper #1 - #4 with Title, abstract, citations, DOI, and pdf (2) Table view: Abstract / Interventions / Outcomes measured / Number of participants (3) Relevant studies and citations. (4) Click on Search for Paper Information to find - Metadata about Sources ( SJR etc.) >Population ( age etc.) >Intervention ( duration etc.) > Results ( outcome, limitations etc.) and > Methodology (detailed study design etc.) (5) Export as BIB or CSV
• How to Search / Extract Data / List of Concept Search -Enter a research question >Workflow: Searching > Summarizing 8 papers> A summary of 4 top papers > Final answers. Each result will show its citation counts, DOI, and a full-text link to Semantic Scholar website for more information such as background citations, methods citation, related papers and more. - List of Concepts search - e.g. adult learning motivation . The results will present a list the related concepts. - Extract data from a pdf file - Upload a paper and let Elicit extract data for you.
• Export Results - Various ways to export results.
• How to Cite - Includes the elicit.org URL in the citation, for example: Ought; Elicit: The AI Research Assistant; https://elicit.org; accessed xxxx/xx/xx

CONSENSUS.APP

ACADEMIC SEARCH ENGINE- using AI to find insights in research papers.

"We are a search engine that is designed to accept research questions, find relevant answers within research papers, and synthesize the results using the same language model technology." <https://consensus.app/home/blog/maximize-your-consensus-experience-with-these-best-practices/>

• Example - Does the death penalty reduce the crime?   /  Fish oil and depression  /    (1) Extracted & aggregated findings from relevant papers. (2) Results may include AIMS, DESIGN, PARTICIPANTS, FINDINGS or other methodological or report components. (3) Summaries and Full Text
• How to Search Direct questions - Does the death penalty reduce the crime? Relationship between two concepts - Fish oil and depression / Does X cause Y? Open-ended concepts - effects of immigration on local economics Tips and search examples from Consensus' Best Practice   
• Synthesize (beta) / Consensus Meter When the AI recognizes certain types of research questions, this functionality may be activated. It will examine a selection of some studies and provide a summary along with a Consensus Meter illustrating their collective agreement. Try this search: Is white rice linked to diabetes? The Consensus Meter reveals the following outcomes after analyzing 10 papers: 70% indicate a positive association, 20% suggest a possible connection, and 10% indicate no link.

Prompt “ write me a paragraph about the impact of climate change on GDP with citations “

CITATIONS IN CONTEXT

Integrated with Research Solutions.

Over 1.2 billion Citation Statements and metadata from over 181 million papers suggested reference.

How does it work? - "scite uses access to full-text articles and its deep learning model to tell you, for a given publication: - how many times it was cited by others - how it was cited by others by displaying the text where the citation happened from each citing paper - whether each citation offers supporting or contrasting evidence of the cited claims in the publication of interest, or simply mention it."   <https://help.scite.ai/en-us/article/what-is-scite-1widqmr/>

EXAMPLE of seeing all citations and citation statements in one place

More information: Scite: A smart citation index that displays the context of citations and classifies their intent using deep learning  

• GPT3.5 by OpenAI. Knowledge cutoff date is September 2021.
• Input/ Output length - ChatGPT-3.5 allows a maximum token limit of 4096 tokens. According to ChatGPT " On average, a token in English is roughly equivalent to 4 bytes or characters. English words are typically around 5 characters long. This means that, very roughly, you could fit around 800 to 1000 English words within 4096 tokens." 
• According to ChatGPT, the generated responses are non-deterministic by default. So if you run the searches again and get slightly or very different results, it's likely due to this factor.
• ChatGPT may find non-existent references.
• According to this study < https://arxiv.org/ftp/arxiv/papers/2304/2304.06794.pdf > "ChatGPT cites the most-cited articles and journals, relying solely on Google Scholar's citation counts" within the field of environmental science.  
• Example - "INTERVIEW WITH CHATGPT" as a Research Method & Teaching Tool Some researchers began to use this approach to obtain their research data. Try this Google Scholar search link "interview with ChatGPT"   or see two articles below: (1) Chatting about ChatGPT: how may AI and GPT impact academia and libraries? BD Lund, T Wang - Library Hi Tech News, 2023 (2) An interview with ChatGPT: discussing artificial intelligence in teaching, research, and practice , G Scaringi, M Loche - 2023  

Increased risk of sudden infant death syndrome (SIDS) Increased risk of premature birth Increased risk of low birth weight Increased risk of respiratory problems in newborns Increased risk of respiratory problems in infants exposed to secondhand smoke Increased risk of developing asthma and other respiratory illnesses later in life for infants exposed to secondhand smoke [Note : ChatGPT may generate non-existent references or false knowledge. To find out why Large Language Models hallucinate, check out this Wiki article: Hallucination (artificial intelligence) and this blog post -   A Gentle Introduction to Hallucinations in Large Language Models by Adrian Tam ]

Infant death Neonatal mortality (referring specifically to deaths within the first 28 days of life) Perinatal mortality (referring to deaths occurring during the period from 22 completed weeks of gestation to 7 completed days after birth) Early childhood mortality (referring to deaths occurring within the first five years of life) Child mortality (referring to deaths occurring before the age of 18) [Press the Regenerate button to get more.]

• Example -  RELATED WORDS What are the related words of infant mortality? Neonatal mortality, Post-neonatal mortality, Stillbirths, Low birth weight, Malnutrition, Infectious diseases, Vaccination, Maternal health,  Access to healthcare, Poverty, Social inequality, Sanitation, Hygiene, Water quality, Childbirth complications, Congenital abnormalities, Birth defects, Maternal age, Under-five mortality, Child mortality, Perinatal mortality, Preterm birth, Low birth weight, Sudden infant death syndrome (SIDS), Maternal mortality, Postnatal care, Malnutrition, Immunization, Access to healthcare, Clean water and sanitation, Congenital anomalies, Infant health disparities, Infant mortality rate (IMR), Infant survival.  

(("Infant Death"[Mesh] OR "Sudden Infant Death Syndrome"[Mesh] OR "Infant Mortality"[Mesh] OR "Perinatal Mortality"[Mesh] OR "Neonatal Mortality"[Mesh] OR "Fetal Mortality"[Mesh] OR "Infant Death" OR "Sudden Infant Death Syndrome" OR "Infant Mortality" OR "Perinatal Mortality" OR "Neonatal Mortality" OR "Fetal Mortality") AND ("Risk Factors"[Mesh] OR "Etiology"[Mesh] OR "Prevention and Control"[Mesh] OR "Interventions"[Mesh] OR "Public Health"[Mesh] OR "Health Disparities"[Mesh] OR "Healthcare Disparities"[Mesh] OR "Poverty"[Mesh] OR "Malnutrition"[Mesh] OR "Infection"[Mesh] OR "Congenital Abnormalities"[Mesh] OR "Prematurity"[Mesh] OR "Low Birth Weight"[Mesh] OR "Maternal Education"[Mesh] OR "Maternal Health"[Mesh] OR "Risk Factors" OR "Etiology" OR "Prevention and Control" OR "Interventions" OR "Public Health" OR "Health Disparities" OR "Healthcare Disparities" OR "Poverty" OR "Malnutrition" OR "Infection" OR "Congenital Abnormalities" OR "Prematurity" OR "Low Birth Weight" OR "Maternal Education" OR "Maternal Health")) Comments and Suggestions: #1 -This search string may not work on all database platforms. For example, Medline (OVID) may not be able to run it, Medline & CINAHL (EBSCO) can run it without any problems while PubMed will run it but it will indicate which MeSH terms are not indexed. See PubMed's search History and Details under Advanced . #2 - This is just one possible combination. You may need to adjust it based on your task and the databases you are using . #3 - It might be easier if you break the task into 3 parts: ask for synonyms > ask for thesauri terms > and ask it to combine them.

Examples - DATABASE RECOMMENDATIONS Heath Science - Which databases to use to find articles on the EFFECTS OF MATERNAL SMOKING ON INFANT DEATH? PubMed: This is a database of biomedical literature from the National Library of Medicine. It includes articles from medical journals and other sources on a wide range of health-related topics. Embase: This is a database of biomedical literature that covers more international and pharmaceutical content than PubMed. It includes articles from medical journals and other sources on a wide range of health-related topics. Cochrane Library : This is a collection of databases that provides systematic reviews and meta-analyses of healthcare interventions, including interventions related to maternal smoking and infant death. Scopus: This is a multidisciplinary database that covers research from a wide range of fields, including health sciences. It includes articles from scientific journals, books, and conference proceedings. Web of Science: This is a database that covers research from a wide range of fields, including health sciences. It includes articles from scientific journals, books, and conference proceedings. CINAHL: This is a database of nursing and allied health literature, including articles on maternal smoking and infant death. [Note: The response above was copied and pasted directly from ChatGPT. No editing was added.] Education -- I am doing a meta-analysis on k-12 STEM education. What databases should I use? ERIC : This is a database of education research and information, including articles from journals, reports, and conference papers. It is a commonly used database for educational research. Education Source : This is a comprehensive database of education research and information, including articles from journals, books, and conference papers. Web of Science: This is a multidisciplinary database that covers research from a wide range of fields, including education. It includes articles from scientific journals, books, and conference proceedings. Scopus: This is another multidisciplinary database that covers research from a wide range of fields, including education. It includes articles from scientific journals, books, and conference proceedings. PsycINFO: This is a database of psychology research, including articles from journals, books, and dissertations. It can be useful for research on STEM education that includes psychological factors. Cochrane Library : This is a collection of databases that provides systematic reviews and meta-analyses of healthcare interventions including interventions related to education. [Author's note: You could also ask for the GREY LITERATURE sources related to the topic above,]

Example: ACT-AS-AN-EXPERT PROMPT Here's an alternative way to consolidate all the questions into a single consultation by using this popular Act-As-An-Expert prompt, aka, system message prompt:  I am going to conduct a systematic review on games and mathematics related to K-12 education  I want you to act as a systematic review expert . I will ask you some questions related to my research topic and you advise me on how to do it. 

[You may ask one question at a time] My first question is "which databases should I use?"  My second question is "what are the relevant subject terms, descriptors and key words?" My third question is "how to do data extraction?" ..... , .... Please note that the above prompts are merely for illustrative purposes. Actual questions may contain more specific instructions. As an example, for the first question, could you provide guidance on how to identify relevant publications? Specifically, can you provide a list of the primary databases and grey literature sources related to my topic?  

Example - GREY LITERATURE Please show me all the resources that offer grey literature for conducting research in the field of education     ERIC (Education Resources Information Center): https://eric.ed.gov/     OpenGrey: http://www.opengrey.eu/     British Education Index: https://www.ebscohost.com/academic/british-education-index     WorldCat: https://www.worldcat.org/     ProQuest Dissertations & Theses Global: https://www.proquest.com/products-services/pqdtglobal.html     EThOS (Electronic Theses Online Service): https://ethos.bl.uk/     Institutional Repositories of universities and academic institutions (search for specific institution repositories)     American Educational Research Association (AERA): https://www.aera.net/     National Education Association (NEA): https://www.nea.org/     Association for Supervision and Curriculum Development (ASCD): http://www.ascd.org/     RAND Corporation: https://www.rand.org/     National Center for Education Research (NCER): https://ies.ed.gov/ncer/     National Center for Education Statistics (NCES): https://nces.ed.gov/     European Educational Research Association (EERA): https://www.eera-ecer.de/     UNESCO International Institute for Educational Planning (IIEP): http://www.iiep.unesco.org/     Organization for Economic Cooperation and Development (OECD): https://www.oecd.org/education/     Education Development Center (EDC): https://www.edc.org/     National Institute for Educational Policy Research (NIER) - Japan: https://www.nier.go.jp/     Australian Council for Educational Research (ACER): https://www.acer.org/     Canadian Education Association (CEA): https://www.cea-ace.ca/ My topic is about stem education. Which grey literature databases should I use?    STEM Education Research Centre (SERC) Grey Literature Database    National STEM Education Research Repository (NSERR)    National Science Digital Library (NSDL)    STEM Education Resource Center (SERC) GrayLit Network    STEM Education Grey Literature Library

See other Comparison Examples under Bing AI.

Examples - ZERO SHOT, ONE SHOT & FEW SHOT PROMPTING for literature review See the examples on the left panel- How to Fine Tune Prompts

Bing Chat / Copilot

• Bing Chat has become Copilot.
• CONVERSATIONAL LLM 4.0 + SEARCH ENGINE. Connects to the Internet.
• Three conversation modes: More Creative, More Balanced or More Precise.
• Comparison - Prompt: My topic is about psychology. Which grey literature databases should I use? More Precise - PsycExtra, NIH Library and links to library guides, .gov and .org websites More Balanced - PsycExtra and three main research guides that mention psychology grey literature sources. More Creative- PsycExtra, LILACS, and Worldcat + definition of grey literature + suggestions on searching for professional association websites, newsletters, magazines, standards and guidelines, and other sources that may contain relevant information for your topic.
• Some examples below were tested in Bing Chat only.  
• Example - factors of infant mortality Infant mortality is a term that refers to the death of infants before their first birthday. The leading causes of infant mortality include birth asphyxia, pneumonia, congenital malformations, term birth complications such as abnormal presentation of the fetus umbilical cord prolapse, or prolonged labor, neonatal infection, diarrhea, malaria, measles, and malnutrition1. Many situational factors lead to infant mortality such as the pregnant woman’s level of education, environmental conditions, political infrastructure, and level of medical support1. In the United States, a primary determinant of infant mortality risk is infant birth weight with lower birth weights increasing the risk of infant mortality. The determinants of low birth weight include socioeconomic, psychological, behavioral and environmental factors2.  
• Example: PEER REVIEWED SOURCES I need to find some peer reviewed articles about infant mortality? The answer includes 5 articles. The first one is “Causes of death and infant mortality rates among full-term births in the United States between 2010 and 2012: An observational study” by Neha Bairoliya and Günther Fink1. A click on  " Tell me more about the first article " leads to this summary: " The first article is titled “Causes of death and infant mortality rates among full-term births in the United States between 2010 and 2012: An observational study” and was published in PLOS Medicine on March 20, 2018. The authors are Neha Bairoliya and Günther Fink. The study used linked birth and death records for the period 2010–2012 to identify the state- and cause-specific burden of infant mortality among full-term infants (born at 37–42 weeks of gestation) in the United States. The researchers found that the full-term infant mortality rate (FTIMR) was 2.2 per 1,000 live births overall and ranged between 1.29 (Connecticut) and 3.77 (Mississippi) at the state level. Sudden unexpected death in infancy (SUDI) accounted for 43% of FTIM; congenital malformations and perinatal conditions accounted for 31% and 11.3% of FTIM, respectively. The largest mortality differentials between states with good and states with poor FTIMR were found for SUDI, with particularly large risk differentials for deaths due to sudden infant death syndrome (SIDS) and suffocation." More searches: Please find five empirical studies focusing on the factors influencing adult learners engaged in online education. Please locate five scholarly articles on stress management among college students site:pubmed.ncbi.nlm.nih.gov

The ERIC Thesaurus contains nearly 12,000 terms, including more than 4,500 descriptors and 7,000 synonyms1. You can use the term “Academic Achievement” or its synonyms such as “Educational Achievement”, “Student Achievement”, “Academic Performance”, “Academic Success”, “Learning Achievement” and more. [ Note : The follow-up responses include some additional sources. See two below: https://www.powerthesaurus.org/academic_achievement https://eric.ed.gov/?ti=Achievement+Gap   including https://eric.ed.gov/?ti=Academic+Achievement |

My topic is about psychology.  Which grey literature databases should I use?

ChatGPT PsycEXTRA : This database is produced by the American Psychological Association (APA) and includes grey literature such as conference papers, white papers, and government reports. OpenGrey : This database provides access to grey literature produced in Europe, including reports, theses, and conference papers Grey Literature Report : This database is produced by The New York Academy of Medicine and includes reports, conference papers, and other grey literature on health and biomedical topics, including psychology. ProQuest Dissertations and Theses Global : This database provides access to graduate-level research, including theses and dissertations related to psychology. Google Scholar: While not exclusively a grey literature database, Google Scholar can be a useful tool for finding conference papers and other grey literature related to psychology. National Technical Information Service (NTIS) : This database provides access to technical reports produced by U.S. government agencies, including those related to psychology. Bing AI   - [asked in the Precise mode] For psychology, some recommended grey literature databases include PsycEXTRA and PsyArXiv . PsycEXTRA is a database of grey literature material relating to psychology, behavioral sciences and health. PsyArXiv is a psychology archive designed to facilitate rapid dissemination of psychological research. What other models have been developed based on SERVQUAL?

ChatGPT SERVPERF: This is a modified version of SERVQUAL that measures service performance instead of service quality. It was developed by Cronin and Taylor (1992) and is based on the same five dimensions as SERVQUAL. SERVPERF-GAP: This model, developed by Caruana and Ewing (2006), extends the SERVQUAL model to include three additional gaps that can occur when customers evaluate service quality. E-SERVQUAL: This model, developed by Parasuraman et al. (2005), adapts the SERVQUAL scale to measure the quality of online services. SERVCO: This is a modified version of SERVQUAL that includes a sixth dimension, "cost," in addition to the five original dimensions. It was developed by Sureshchandar et al. (2002) to address the importance of cost in service quality evaluation. SERVQ-HC: This is a modified version of SERVQUAL that is specifically designed for evaluating the quality of health care services. It was developed by Cronin et al. (2000) and includes 20 items across five dimensions. Bing AI The Nordic model of service quality2. The hierarchical model of service quality3. The American Customer Satisfaction Index (ACSI)4.

     Bing AI Sidebar - CHAT, THINK, & WRITE

• Examples of Using AI sidebar to Recap Google Scholar findings

Chat - If you highlight a study in the search results of Google Scholar, you will be prompted with a question by AI sidebar asking if you want to send the selected text to it. If you choose to do so, you can then select either  EXPLAIN or EXPAND to learn more about the chosen article. It's worth noting that EXPAND provides more detailed information. You can then click on any of the follow-up links to delve further into the selected article or topic. If the text of the chosen study is written in a foreign language, the AI sidebar can also provide an explanation of the text in English. Another option for extracting the key points from a selected article is to submit the URL of the PDF. Insights - Use the insights button to get more ideas about the topic. Compose - Tell the Edge sidebar your topic and let its Compose tool to generate a draft for you. Google Scholar Button- See also the Plugins and Extension page for how to perform a combined search using Google, Google Scholar, and ChatGPT .

Example: FINDING SIMILAR ARTICLES Find similar articles like this one - [then paste in the abstract ] Note - Use this prompt with Bing chat since real-time web info is required.

Examples: SUMMARIZE + REFERENCE Use a basic prompt like " Please summarize . ..." or add a REFERENCE TEXT that provides the necessary information about what you want it to focus or how the summary or resolution should look like. The reference could be a quote, an excerpt, a partial sentence, a conversation, an abstract, a code snippet and so on. Formula : Specify a role > assign a task > provide a reference text. Example#1 - Scholarly Journal Quote

STEP 1 Prompt: As a research assistant specializing in renewable energy, your task is to locate comparable research studies based on the provided abstract from an article: "This paper discusses the perspective of renewable energy (wind, solar, wave and biomass) in the making of strategies for a sustainable development. Such strategies typically involve three major technological changes: energy savings on the demand side, efficiency improvements in the energy production, and replacement of fossil fuels by various sources of renewable energy."

Bing Chat then found 3 documents: the ScienceDirect paper where the abstract came from and two other documents from un.org and frontiersin.org respectively.

STEP 2 Prompt: Please synthesize the findings of the three studies provided above.

Bing Chat: "Sure! Here is a synthesis of the findings from the three studies:

All three studies emphasize the importance of renewable energy in achieving sustainable development and mitigating climate change. They all discuss various strategies and actions that can be taken to increase the use of renewable energy sources and reduce reliance on fossil fuels.

Henrik Lund’s paper discusses the perspective of renewable energy (wind, solar, wave and biomass) in the making of strategies for a sustainable development. Such strategies typically involve three major technological changes: energy savings on the demand side, efficiency improvements in the energy production, and .....

The United Nations outlines five critical actions the world needs to prioritize now to transform our energy systems and speed up the shift to renewable energy: Make renewable energy technology a global public good, Improve global access to components and raw materials......

“Sustainable Energy Transition for Renewable and Low Carbon Grid” discusses that a sustainable global electricity transition will entail increased use of renewable energy sources particularly wind and solar, nuclear energy as a low carbon energy source, electrification of transport and.......

In summary , these studies suggest that transitioning to renewable energy sources is crucial for sustainable development and mitigating climate change. This can be achieved through various strategies such as increasing energy efficiency, improving access to renewable technology and materials, leveling the playing field for renewables, ending fossil fuel subsidies, and redirecting investments towards renewables." Example #2 Y ou are an expert researcher, given the following article, please discuss its key findings. Reference: How Large Language Models are Transforming Machine-Paraphrased Plagiarism at https://arxiv.org/abs/2210.03568 This is another way to put REFERENCE TEXT. As a side not, role specifying or the use of persona is important as it can impact the level of responses obtained. Just imagine if the designated role were " You are a high school student... ." Example #3 Please read this article and summarize it f or me - "Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making" and then find 5 similar studies and please also summarize each for me.

CONNECTED PAPERS

• RELATED STUDIES
• Uses visual graphs or other ways to show relevant studies. The database is connected to the Semantic Scholar Paper Corpus which has compiled hundreds of millions of published papers across many science and social science fields.
• See more details about how it works .  
• Example - SERVQUAL and then click on SELECT A PAPER TO BUILD THE GRAPH > The first paper was selected. Results: (1) Origin paper - SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality + Connected papers with links to Connected Papers / PDF / DOI or Publisher's site / Semantic Scholar / Google Scholar. (2) Graph showing the origin paper + connected papers with links to the major sources . See above. (3) Links to Prior Works and Derivative Works See the detailed citations by Semantic Scholar on the origin SERVQUAL paper on the top of this page within Semantic Scholars.
• How to Search Search by work title. Enter some keywords about a topic.
• Download / Save Download your saved Items in Bib format.

PAPER DIGEST

• SUMMARY & SYNTHESIS
• " Knowledge graph & natural language processing platform tailored for technology domain . <"https://www.paperdigest.org/> Areas covered: technology, biology/health, all sciences areas, business, humanities/ social sciences, patents and grants ...

• LITERATURE REVIEW - https://www.paperdigest.org/review/ Systematic Review - https://www.paperdigest.org/literature-review/
• SEARCH CONSOLE - https://www.paperdigest.org/search/ Conference Digest - NIPS conference papers ... Tech AI Tools: Literature Review  | Literature Search | Question Answering | Text Summarization Expert AI Tools: Org AI | Expert search | Executive Search, Reviewer Search, Patent Lawyer Search...

Daily paper digest / Conference papers digest / Best paper digest / Topic tracking. In Account enter the subject areas interested. Daily Digest will upload studies based on your interests.

RESEARCH RABBIT

• CITATION-BASED MAPPING: SIMILAR / EARLY / LATER WORKS
• " 100s of millions of academic articles and covers more than 90%+ of materials that can be found in major databases used by academic institutions (such as Scopus, Web of Science, and others) ." See its FAQs page. Search algorithms were borrowed from NIH and Semantic Scholar.

The default “Untitled Collection” will collect your search histories, based on which Research Rabbit will send you recommendations for three types of related results: Similar Works / Earlier Works / Later Works, viewable in graph such as Network, Timeline, First Authors etc.

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• 10 September 2018
• Correction 05 October 2018

How AI technology can tame the scientific literature

• Andy Extance 0

Andy Extance is a freelance writer based in Exeter, UK.

You can also search for this author in PubMed   Google Scholar

Illustration by The Project Twins

When computer scientist Christian Berger’s team sought to get its project about self-driving vehicle algorithms on the road, it faced a daunting obstacle. The scientists, at the University of Gothenburg in Sweden, found an overwhelming number of papers on the topic — more than 10,000 — in a systematic literature review. Investigating them properly would have taken a year, Berger says.

Luckily, they had help: a literature-exploration tool powered by artificial intelligence (AI), called Iris.ai. Using a 300-to-500-word description of a researcher’s problem, or the URL of an existing paper, the Berlin-based service returns a map of thousands of matching documents, visually grouped by topic. The results, Berger says, provide “a quick and nevertheless precise overview of what should be relevant to a certain research question”.

Iris.ai is among a bevy of new AI-based search tools offering targeted navigation of the knowledge landscape. Such tools include the popular Semantic Scholar, developed by the Allen Institute for Artificial Intelligence in Seattle, Washington, and Microsoft Academic. Although each tool serves a specific niche, they all provide scientists with a different look at the scientific literature than do conventional tools such as PubMed and Google Scholar. Many are helping researchers to validate existing scientific hypotheses. And some, by revealing hidden connections between findings, can even suggest new hypotheses for guiding experiments.

Such tools provide “state-of-the-art information retrieval”, says Giovanni Colavizza, a research data scientist at the Alan Turing Institute in London, who studies full-text analysis of scholarly publications. Whereas conventional tools act largely as citation indices, AI-based ones can offer a more penetrating view of the literature, Colavizza says.

That said, these tools are often expensive, and limited by the fraction of the scientific literature they search. “They are not meant to give you an exhaustive search,” says Suzanne Fricke, an animal-health librarian at Washington State University in Pullman, who has written a resource review on Semantic Scholar ( S. Fricke J. Med. Lib. Assoc. 106 , 145–147; 2018 ). Some, for example, “are meant to get you quickly caught up on a topic, which is why they should be used in conjunction with other tools”. Berger echoes this sentiment: “Blindly using any research engine doesn’t answer every question automatically.”

Teaching science to machines

AI-based ‘speed-readers’ are useful because the scientific literature is so vast. By one estimate, new papers are published worldwide at a rate of 1 million each year — that’s one every 30 seconds. It is practically impossible for researchers to keep up, even in their own narrow disciplines. So, some seek to computationally tame the flood.

The algorithms powering such tools typically perform two functions — they extract scientific content and provide advanced services, such as filtering, ranking and grouping search results. Algorithms extracting scientific content often exploit natural language processing (NLP) techniques, which seek to interpret language as humans use it, Colavizza explains. Developers can use supervised machine learning, for example — which involves ‘tagging’ entities, such as a paper’s authors and references, in training sets to teach algorithms to identify and extract them.

To provide more-advanced services, algorithms often construct ‘knowledge graphs’ that detail relationships between the extracted entities and show them to users. For example, the AI could suggest that a drug and a protein are related if they’re mentioned in the same sentence. “The knowledge graph encodes this as an explicit relationship in a database, and not just in a sentence on a document, essentially making it machine readable,” Colavizza says.

Iris.ai takes a different approach, Colavizza notes, grouping documents into topics defined by the words they use. Iris.ai trawls the CORE collection, a searchable database of more than 134 million open-access papers, as well as journals to which the user’s library provides access. The tool blends three algorithms to create ‘document fingerprints’ that reflect word-usage frequencies, which are then used to rank papers according to relevance, says Iris.ai chief technology officer Viktor Botev.

The result is a map of related papers, but eventually the company plans to supplement those results by identifying hypotheses explored in each paper as well. It is also developing a parallel, blockchain-based effort called Project Aiur, which seeks to use AI to check every aspect of a research paper against other scientific documents, thus validating hypotheses.

Colavizza says that tools such as Iris.ai — free for basic queries, but costing upwards of €20,000 (US$23,000) a year for premium access, which allows more-nuanced searches — can accelerate researchers’ entry into new fields. “It facilitates initial exploration of the literature in a domain in which I’m marginally familiar,” he says.

Experts seeking deeper insights into their own specialities might consider free AI-powered tools such as Microsoft Academic or Semantic Scholar, Colavizza suggests. Another similar option is Dimensions, a tool whose basic use is free but which costs to search and analyse grant and patent data, as well as to access data using the programmable Dimensions Search Language. (Dimensions is created by technology firm Digital Science, operated by the Holtzbrinck Publishing Group, which also has a majority share in Nature ’s publisher.)

Semantic Scholar has a browser-based search bar that closely mimics engines such as Google. But it gives more information than Google Scholar to help experts to prioritize results, Colavizza says. That includes popularity metrics, topics such as data sets and methods, and the exact excerpt in which text is cited. “I was very surprised to find that they also capture indirect citations,” Colavizza adds — such as when a method or idea is so well established that researchers don’t refer to its origin.

Doug Raymond, Semantic Scholar’s general manager, says that one million people use the service each month. Semantic Scholar uses NLP to extract information while simultaneously building connections to determine whether information is relevant and reputable, Raymond says. It can identify non-obvious connections, such as methodologies in computer science that are relevant to computational biology, he adds, and it can help to identify unsolved problems or important hypotheses to validate or disprove. Currently, Semantic Scholar incorporates more than 40 million documents from computer and biomedical science, and its corpus is growing, says Raymond. “Ultimately, we’d like to incorporate all academic knowledge.”

For other tools, such as SourceData from the European Molecular Biology Organization (EMBO) in Heidelberg, Germany, experimental data are a more central concern. As chief editor of Molecular Systems Biology , an EMBO publication, Thomas Lemberger wants to make the data underlying figures easier to find and interrogate. SourceData therefore delves into figures and their captions to list biological objects involved in an experiment, such as small molecules, genes or organisms. It then allows researchers to query those relationships, identifying papers that address the question. For instance, searching, ‘Does insulin affect glucose?’ retrieves ten papers in which the “influence of insulin (molecule) on glucose (molecule) is measured”.

SourceData is at an early stage, Lemberger says, having generated a knowledge graph comprising 20,000 experiments that were manually curated during the editing process for roughly 1,000 articles. The online tool is currently limited to querying this data set, but Lemberger and his colleagues are training machine-learning algorithms on it. The SourceData team is also working on a modified neuroscience-focused version of the tool with an interdisciplinary neuroscience consortium led by neurobiologist Matthew Larkum at Humboldt University in Berlin. Elsewhere, IBM Watson Health in Cambridge, Massachusetts, announced in August that it will combine its AI with genomics data from Springer Nature to help oncologists to define treatments. ( Nature ’s news team is editorially independent of its publisher.)

Hypothetically useful

Among those embarking on hypothesis generation are the roughly 20 customers of Euretos, based in Utrecht, the Netherlands. Arie Baak, who co-founded Euretos, explains that the company sells tools to industry and academia, mainly for biomarker and drug-target discovery and validation, for prices he did not disclose.

Euretos uses NLP to interpret research papers, but this is secondary to the 200-plus biomedical-data repositories it integrates. To understand them, the tool relies on the many ‘ontologies’ — that is, structured keyword lists — that life scientists have created to define and connect concepts in their subject areas.

Baak demonstrates by searching for a signalling protein called CXCL13. Above the resulting publication list are categories such as ‘metabolites’ or ‘diseases’. The screen looks much like Google Scholar or PubMed at this stage, with an ordered list of results. But clicking on a category reveals extra dimensions. Selecting ‘genes’, for instance, pulls up a list of the genes associated with CXCL13, ranked by how many publications mention them; another click brings up diagrams illustrating connections between CXCL13 and other genes.

Researchers at the Leiden University Medical Centre (LUMC) in the Netherlands have shown that this approach can yield new hypotheses, identifying candidate diseases that existing drugs might treat. The team presented its results at the Semantic Web Applications and Tools for Health Care and Life Sciences meeting in Rome in December 2017. They have also used Euretos to identify gene-expression changes in a neurological disorder called spinocerebellar ataxia type 3 ( L. Toonen et al. Mol. Neurodegener. 13 , 31; 2018 ).

So, should researchers worry that AI-based hypothesis generation could put them out of a job? Not according to Colavizza. Hypothesis generation is a “very challenging ambition”, he says, and improvements initially will be incremental. The hypotheses suggested so far are therefore “mostly in the realm of the relatively unsurprising ones”, Colavizza says.

That will probably change, of course. But surprising or not, computer-generated hypotheses must still be tested. And that requires human researchers. “One should never believe an auto-generated hypothesis first-hand without investigating the underlying evidence,” warns LUMC researcher Kristina Hettne. “Even though these tools can assist in collecting the known evidence, experimental validation is a must.”

Nature 561 , 273-274 (2018)

doi: https://doi.org/10.1038/d41586-018-06617-5

Updates & Corrections

Correction 05 October 2018 : An earlier version of this Toolbox referred to the CORE repository by its old name, Connecting Repositories.

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AI Assistance in Academia for Searching Credible Scholarly Sources

The journey of academia is a grand quest for knowledge, more specifically an adventure to find the right information through credible sources, and that’s where scholarly sources walk in. As the name suggests, it simply means that such sources are written by scholars and experts of a specialized field. These sources are in the form of journal articles, books, conference publications, or websites. Such resources undergo a stringent peer-review process by a panel of subject matter experts. Thus, the findings presented are credible and refined. In contrast, popular sources, such as newspapers, magazines, and blogs, often lack the meticulous scrutiny of peer review and may prioritize approachability over technical accuracy.

Let’s explore the key points of difference between scholarly sources and popular sources:

Table of Contents

Why Cite Scholarly Sources When Writing Your Research Paper?

Selecting scholarly sources is a strategic decision that affects your research outcome, as it builds the foundation of your academic work. It plays a crucial role for individuals at every stage of their academic journey, benefiting both early-career and established researchers. It allows scholars to actively participate in academic conversations by sharing valuable insights in their field, inspiring others in the research area to make advancements. Furthermore, for a university student, making their first stride in scholarly research, it enhances their knowledge and serves as a gateway to join the scholarly community.

Curious about the nuances of literature review in academia? Take our short quiz now to deepen your understanding of this essential academic practice.

Fill the Details to Check Your Score

Exploring Barriers in Scholarly Literature Search

Exploring today’s vast information landscape is like finding a needle in a haystack. This mirrors the challenges researchers face with the overwhelming number of scholarly articles in today’s digital age. According to the National Center for Science and Engineering Statistics , scientists publish nearly 3 million publications each year. A simple query search on platforms like Google Scholar can result in an overwhelming number of articles, posing a challenge to navigate through them and gain a comprehensive overview. Furthermore, such scholarly search platforms often fail to reveal new findings in a particular field as the system tends to favor highly cited , older articles.

Modern researchers and students face further complex challenges as science becomes more specialized, introducing peculiar jargon and keywords. This coupled with the multidisciplinary nature of research, can result in overlooking valuable findings from other fields due to specific jargon and knowledgebase constraints. Other challenges include identifying and selecting relevant resources due to the diverse formats in which information is presented (journal articles, preprints, conference posters, etc.), selecting which literature to choose with precision to prevent unintended plagiarism , and software limitations in managing literature sources in different formats.

Understanding the Use of AI Research Assistance Tools for Reliable Scholarly Sources

In this world of the internet, where a single click generates thousands of search results, AI research assistance tools can guide you through this chaos to the right scholarly source. These tools automate the process of identifying reliable papers, finding and summarizing relevant sections of a research paper , analyzing it to bridge knowledge gaps, and generating potential project ideas. Listed below are the pros and cons of such tools to help you make informed decisions when incorporating these tools into your research workflow:

4 Essential Research Assistance AI Tools

Currently, there are numerous AI-based tools with a wide range of capabilities for literature review assistance , each tailored to specific requirements. It is imperative to make an informed decision based on certain evaluation parameters that sets each of these tools apart. Simplifying this choice for you, we have compared 4 most useful AI research assistance tools to understand their distinctiveness:

Here is a comprehensive analysis of 4 most useful AI research assistance tools for a thorough understanding of their capabilities:

1. Semantic Scholar

Semantic scholar is an AI-powered search engine that:

• Conducts contextual analysis of papers.
• Offers advanced citation analysis and automates the process of identifying relevant research papers from its database of over 200 million papers for literature reviews.
• Bridges knowledge gaps and prompts project ideas.

2. Enago Read

Enago Read offers several features to enhance the experience of researchers for literature exploration and research process. They include:

• Summarizes key sections of papers, allowing for quick insight into the content of a research article.
• Supports inline attachments, ensuring a seamless organization of project-related files.
• Provides AI-powered suggestions to discover related research material and stay updated with relevant news from the large data repository of 170+ million research papers.
• Promotes enhanced collaboration and structured note-taking for better team communication, accelerating the process of literature review and critical reading.
• Streamlines the management of references , contributing to a more efficient and organized research workflow.

Scite is an AI research tool that:

• Provides services to enhance the research experience, including smart citations that support, contrast, and mention relevant content
• Presents citation contexts with accompanying text excerpts, allowing for a more in-depth understanding from over 181 million papers.
• Facilitates thorough literature reviews, making it a valuable tool for researchers engaging in systematic reviews and meta-analyses

4. Connected Papers

Connected Papers aids in identifying gaps in literature reviews in the following ways:

• Provides valuable insights for your scholarly venture
• Visualizes the research landscape, helping you identify key papers and trends
• Deepens your understanding of research relationships by exploring paper interconnections

While many AI research assistant tools help in identifying and storing scholarly sources, the real challenge is in efficiently connecting them. You must try and choose a platform that acts as an all-in-one solution. A platform that promotes your research needs, enabling you to execute literature analysis, collaborate with your team, identify research gaps, draft, and explore the journey of research.

Have you used any of the mentioned AI research assistance tools to find reliable scholarly sources and analyze them? Let us know how this article has contributed to your journey in finding the right research source for you. Also, don’t forget to share your preferred AI tool for academic research on Enago Academy’s Open Platform .

Frequently Asked Questions

Scholarly sources are academic sources written by subject-matter experts, and are subject to the process of peer review. This process ensures that the information in such sources is updated, scientifically accurate, and of excellent quality.

The examples of scholarly sources include journal articles, books, conference publications, dissertations, and even websites.

Citing sources is ethically important as it enhances the credibility of your work and prevents plagiarism. It also ensures that the credit is given where it is due.

Scholarly sources are important for academic research as they present accurate and updated information that is free of bias and is objective in nature.

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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)

As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change. 

Make no mistake, AI is here to stay!

Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.

These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!

The influence of AI in scientific and academic research is an exciting development, opening the doors to more efficient, comprehensive, and rigorous exploration.

This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.

I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!

Here is everything you need to know about AI for academic research and the ones I have personally trialed on my Youtube channel.

Best ChatGPT interface – Chat with PDFs/websites and more

I get more out of ChatGPT with HeyGPT . It can do things that ChatGPT cannot which makes it really valuable for researchers.

Use your own OpenAI API key ( h e re ). No login required. Access ChatGPT anytime, including peak periods. Faster response time. Unlock advanced functionalities with HeyGPT Ultra for a one-time lifetime subscription

AI literature search and mapping – best AI tools for a literature review – elicit and more

Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!

AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.

They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.

With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.

• Elicit –  https://elicit.org
• Supersymmetry.ai: https://www.supersymmetry.ai
• Semantic Scholar: https://www.semanticscholar.org
• Connected Papers –  https://www.connectedpapers.com/
• Research rabbit – https://www.researchrabbit.ai/
• Laser AI –  https://laser.ai/
• Litmaps –  https://www.litmaps.com
• Inciteful –  https://inciteful.xyz/
• Scite –  https://scite.ai/
• System –  https://www.system.com

If you like AI tools you may want to check out this article:

• How to get ChatGPT to write an essay [The prompts you need]

AI-powered research tools and AI for academic research

AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research. 

These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from. 

Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.

The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!

• Bit AI –  https://bit.ai/
• Consensus –  https://consensus.app/
• Exper AI –  https://www.experai.com/
• Hey Science (in development) –  https://www.heyscience.ai/
• Iris AI –  https://iris.ai/
• PapersGPT (currently in development) –  https://jessezhang.org/llmdemo
• Research Buddy –  https://researchbuddy.app/
• Mirror Think – https://mirrorthink.ai

AI for reading peer-reviewed papers easily

Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in. 

These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!

They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.

With AI, deciphering complex citations and accelerating research has never been easier.

• Open Read –  https://www.openread.academy
• Chat PDF – https://www.chatpdf.com
• Explain Paper – https://www.explainpaper.com
• Humata – https://www.humata.ai/
• Lateral AI –  https://www.lateral.io/
• Paper Brain –  https://www.paperbrain.study/
• Scholarcy – https://www.scholarcy.com/
• SciSpace Copilot –  https://typeset.io/
• Unriddle – https://www.unriddle.ai/
• Sharly.ai – https://www.sharly.ai/

AI for scientific writing and research papers

In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.

Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.

Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.

• Paper Wizard –  https://paperwizard.ai/
• Jenny.AI https://jenni.ai/ (20% off with code ANDY20)
• Wisio – https://www.wisio.app

AI academic editing tools

In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.

Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.

• Paper Pal –  https://paperpal.com/
• Writefull –  https://www.writefull.com/
• Trinka –  https://www.trinka.ai/

AI tools for grant writing

In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.

These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.

Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.

Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.

• Granted AI – https://grantedai.com/
• Grantable – https://grantable.co/

Free AI research tools

There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.

The best free ones at time of writing are:

• Elicit – https://elicit.org
• Connected Papers – https://www.connectedpapers.com/
• Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
• Consensus – https://consensus.app/

Wrapping up

The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.

With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.

The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.

These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.

They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.

Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.

And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.

We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.

Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!

Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

Thank you for visiting Academia Insider.

We are here to help you navigate Academia as painlessly as possible. We are supported by our readers and by visiting you are helping us earn a small amount through ads and affiliate revenue - Thank you!

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AI Literature Review Generator

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The Best 8 AI-Powered Tools for Literature Review

In this blog post, I will explore the benefits of using best AI-powered tools for literature review . Literature survey is a crucial step in any research work, but it can also be a daunting and time-consuming task.

You have to find, read, and evaluate hundreds or thousands of research papers, synthesize the existing knowledge, and identify the gaps and opportunities for your research.

Fortunately, there are some AI-powered apps that can help you with literature survey and make it easier and faster. In order to automate the tasks such as searching for articles, identifying relevant papers, and summarizing papers the apps use the artificial intelligence.

Besides, this saves you a significant amount of time and effort. It lets you focus on the more creative and strategic aspects of your research work.

Here are some of the best 8 AI-powered apps for literature review that you should try:

Research Rabbit

Research Rabbit is an AI-powered app that helps researchers find, organize, and analyze research papers.

It is a free tool that can be used by anyone, regardless of their research experience.

Research Rabbit uses AI to scan the web for relevant scholarly articles. In addition to that the AI-based tool allows you to save and organize articles in your own personal library.

To use Research Rabbit, you first need to create an account. Once you have created an account, you can start searching for research papers. You can search by keyword, topic, or author.

Once you have found the papers you are interested in, you can add them to your collections. Collections are a way to organize your research papers. You can create as many collections as you need.

Research Rabbit- Link

Rayyan is an AI-powered app that helps researchers conduct systematic literature review s.

A systematic literature review is a comprehensive research process that involves identifying, evaluating, and synthesizing all the available research on a particular topic.

It helps you work quickly through even your largest reviews. It lets you de-duplicate, screen, and organize references, as well as collaborate with your team and generate reports.

You can use Rayyan to import references from various sources, apply inclusion and exclusion criteria, assign labels and ratings, and export your data for further analysis.

Rayyan- Link

Scholarcy is an AI-powered app that can help you with your academic reading. It can automatically summarize articles, create flashcards, and generate bibliographies.

Scholarcy can also help you find related research and identify key information in articles.

In order to use Scholarcy, you first need to create an account. Once you have created an account, you can start adding articles to your library.

Additionally, you can add academic papers from a variety of sources like Google Scholar, PubMed, and your personal library.

Once you have added scholarly articles to your library, you can start using Scholarcy’s features. To summarize an article, simply click on the “Summarize” button.

Scholarcy will then generate a summary of the article in a few seconds.

Overall, Scholarcy is a valuable tool that can help you with your academic reading. If you are looking for a way to save time, improve your understanding, and increase your productivity, Scholarcy is a great option.

Scholarcy- Link

Lateral is an AI-powered app that can help you with your academic research. It can help you find relevant research, organize your findings, and write better papers.

Lateral uses artificial intelligence to analyze research papers and identify key concepts, relationships, and trends. This information can then be used to help you find relevant research, organize your findings, and write better papers.

It helps you organize, search, and save information from collections of articles. You can import articles from various sources, create tags and notes, and use natural language queries to find specific information in your collections.

You can use Lateral to manage your literature review workflow , keep track of your sources and citation s, and access your information anytime and anywhere.

Lateral – Link

Scite is a free, open-source AI-powered app that helps researchers better discover and understand research articles. Scite does this by providing Smart Citation s, which display the context of a citation and describe whether the article provides supporting or contrasting evidence.

To use Scite, simply go to the Scite website and sign up for an account. Once you have an account, you can start searching for research articles. Scite will display a list of articles that match your search criteria, and you can then click on an article to view its Smart Citation s.

You can use Scite to assess the quality and reliability of the literature, avoid citing unreliable sources, and find evidence for or against any hypothesis.

Scite- Link

If you are looking for a way to quickly and easily find answers from scientific research, you might want to try Consensus AI, a new search engine that uses artificial intelligence to extract and distill findings directly from peer-reviewed studies.

Consensus AI is a powerful tool that can help you find evidence-based answers from scientific research in a fast and easy way.

You can also use various features to refine your search and explore more options. Consensus AI can help you save time and effort in finding reliable information from scientific research.

Consensus- Link

Semantic Scholar

It is a free AI-powered academic search engine that helps you find relevant academic papers faster and easier than traditional methods. It uses natural language processing and machine learning to understand your research needs and provide you with smart filters, citation analysis, and key insights from papers.

You can use Semantic Scholar to discover the most influential and recent scientific literature in your field, compare different methods and results, and track the impact of your own publications.

Semantic Scholar- Link

It is an AI-powered reading assistant that helps you organize, annotate, and collaborate on your research. It helps you fine-tune your literature search, generate summaries, highlight and compare ideas, and share and discuss literature with your team.

You can use RAx to create different projects for your research topics, import articles from various sources, annotate them with notes and comments, and get personalized suggestions for further reading.

RAx Literature Review and Critical Analysis Tool-  Link

It is an AI-powered research assistant that can help you with tasks such as finding relevant articles, summarizing articles, and generating research ideas.

A comprehensive platform for all your research processing: Smart search and a wide range of smart filters, reading list analysis, auto-generated summaries, autonomous extraction and systematizing of data

Iris AI -Your Research Workspace- Link

These are some of the best 8 AI-powered apps for literature review that can help you save time and effort, improve your understanding and writing skills, and produce high-quality research. Try them out today and see how they can supercharge your literature review process.

It is important to note that AI-powered apps are not a replacement for hum an judgment. However, they can be a valuable tool for helping you to find relevant research articles, identify important concepts, and track the development of research over time.

If you are interested in trying one of these apps, I encourage you to visit their websites and sign up for a free trial. You can also read reviews from other users to get a better idea of which app is right for you.

If you want to supercharge your literature review with AI, you should try out these 8 apps today. They are easy to use, affordable, and reliable. They will transform your literature review process and help you achieve your research goals faster and easier.

Don’t wait any longer. Start using these AI-powered apps for literature review today and see the difference for yourself. You will be amazed by how much they can help you with your research.

I hope this blog post has been helpful. If you have any questions, please feel free to leave a comment below.

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Revolutionize Your Research with Jenni AI

Literature Review Generator

Welcome to Jenni AI, the ultimate tool for researchers and students. Our AI Literature Review Generator is designed to assist you in creating comprehensive, high-quality literature reviews, enhancing your academic and research endeavors. Say goodbye to writer's block and hello to seamless, efficient literature review creation.

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Discover the Game-Changing Features of Jenni AI for Literature Reviews

Advanced AI Algorithms

Jenni AI utilizes cutting-edge AI technology to analyze and suggest relevant literature, helping you stay on top of current research trends.

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Idea Generation

Overcome writer's block with AI-generated prompts and ideas that align with your research topic, helping to expand and deepen your review.

Built-In Plagiarism Checker

Ensure the originality of your work with an integrated plagiarism checker, minimizing the risk of accidental plagiarism.

Citation Assistance

Get help with proper citation formats to maintain academic integrity and attribute sources correctly.

Our Pledge to Academic Integrity

At Jenni AI, we are deeply committed to the principles of academic integrity. We understand the importance of honesty, transparency, and ethical conduct in the academic community. Our tool is designed not just to assist in your research, but to do so in a way that respects and upholds these fundamental values.

How it Works

Start by creating your account on Jenni AI. The sign-up process is quick and user-friendly.

Define Your Research Scope

Enter the topic of your literature review to guide Jenni AI’s focus.

Interact with AI-Powered Suggestions

Use Jenni AI’s suggestions to structure your literature review, organizing it into coherent sections.

Plagiarism Verification

Employ the integrated plagiarism checker to ensure the uniqueness of your work.

Citation Guidance

Receive assistance in citing sources correctly, maintaining the academic standard.

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Export your literature review to LaTeX, HTML, or .docx formats

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Frequently asked questions

What exactly does jenni ai do, is jenni ai suitable for all academic disciplines, how does jenni ai ensure the originality of my work, is there a trial period or a free version available.

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Can Jenni AI write my literature review for me?

How often is the literature database updated in Jenni AI?

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Jenni AI: Standing Out From the Competition

In a sea of online proofreaders, Jenni AI stands out. Here’s how we compare to other tools on the market:

Feature Featire

COMPETITORS

Advanced AI-Powered Assistance

Uses state-of-the-art AI technology to provide relevant literature suggestions and structural guidance.

May rely on simpler algorithms, resulting in less dynamic or comprehensive support.

Commitment to Academic Integrity

Strong focus on maintaining academic standards with built-in plagiarism checks and citation assistance.

Not all provide integrated plagiarism tools or may have less emphasis on citation guidelines.

User-Friendly Interface

Designed for ease of use, making it accessible for users with varying levels of tech proficiency.

Interfaces can be complex or less intuitive, posing a challenge for some users.

Transparent and Flexible Pricing

Offers a free trial and clear, flexible pricing plans suitable for different needs.

Pricing structures can be opaque or inflexible, with fewer user options.

Unparalleled Customization

Offers highly personalized suggestions and adapts to your specific research needs over time.

Often provide generic suggestions that may not align closely with individual research topics.

Comprehensive Literature Access

Provides access to a vast and up-to-date range of academic literature, ensuring comprehensive research coverage.

Some may have limited access to current or diverse research materials, restricting the scope of literature reviews.

Ready to Transform Your Research Process?

Don't wait to elevate your research. Sign up for Jenni AI today and discover a smarter, more efficient way to handle your academic literature reviews.

AI Power Literature Review Tools

• AI Search Engines
• AI Research Aids

Subject Guide

AI-Powered Search Tools 

Some of the AI-powered technologies mentioned throughout this guide require a subscription or registration. This guide is meant to be used as a source of information. It is recommended that you assess these tools and the methods for using them on your own specific needs.

Semantic Scholar

Search over 214 million papers from all fields of science, with filters such as journals and conferences, authors, publication types, and date range.

Start with the Highly Influential Citations where the cited publication has a significant impact on the citing publication, determined by a machine-learning model that analyzes factors including the number of citations to a publication and the surrounding context for each.

Identifying the right papers for you can be time-consuming. To save some time, look for TLDRs (Too Long; Didn't Read) on search results pages. TLDRs are super-short summaries of the main objective and results of a paper, generated using expert background knowledge and NLP techniques, available for nearly 60 million papers in computer science, biology, and medicine.

Ask a research question and get back a list of relevant papers from our database of 200 million Find themes and concepts across all the papers For example, what are all of the effects of this drug? What are all of the datasets that have been used? Get a list of the things you’re looking for, synthesized from many papers

Consensus is a search engine that uses AI to find research insights.

Consensus uses a network of AI algorithms to cross-reference and confirm details from numerous sources. It provides reliable and fact-checking information. 

Consensus is not a chatbot,  but we use the same technology throughout the product to help make the research process more efficient.

The current source material used in Consensus comes from the  Semantic Scholar  database, which includes over 200M papers across all domains of science.

ResearchRabbit

ResearchRabbit is a completely novel way to search for papers and authors, monitor new literature, visualize research landscapes, and collaborate with colleagues.

Just like in Spotify, you can add papers to collections. ResearchRabbit learns what you love and improves its recommendations!

Keep up with the latest papers related to your collections! If we’re not confident something’s relevant, we don’t email you—no spam!

Visualize networks of papers and co-authorships. Use graphs as new “jumping off points” to dive even deeper!

Collaborate on collections, or help kickstart someone’s search process! And leave comments as well!

The Paper Discovery tool builds a network of papers from citations, uses network analysis algorithms to analyze the network, and gives you the information you need to quickly get up to speed on that topic. You can find the most similar papers, important papers as well as prolific authors and institutions.

Our second tool was the Literature Connector. Intended for interdisciplinary scholars trying to bridge two domains, it allows you to enter two papers and it will give you an interactive visualization showing you how they are connected by the literature. From there you can send the papers you find into our Paper Discovery tool to find other relevant literature that happened to not be the "shortest path" between the two papers.

NotebookLM is an experimental product designed to use the power and promise of language models paired with your existing content to gain critical insights, faster. Think of it as a virtual research assistant that can summarize facts, explain complex ideas, and brainstorm new connections — all based on the sources you select.

A key difference between NotebookLM and traditional AI chatbots is that NotebookLM lets you “ground” the language model in your notes and sources. Source-grounding effectively creates a personalized AI that’s versed in the information relevant to you. Starting today, you can ground NotebookLM in specific Google Docs that you choose.

• Get a summary of your document.
• Ask questions about your documents.
• Generate ideas based on your documents.

You have too many files to read as is. Stop wasting time. Instead, have the Docalysis AI read your files and answer your questions. Save time by having an AI answer questions about your files within seconds.

Docalysis is an AI-powered platform that answers any question you have about your documents. Simply upload a PDF, TXT, CSV, or DOCX file and let Docalysis analyze it for you. Then have a conversation with Docalysis to get answers to your questions. 

Search over 200M+ peer-reviewed articles in our database.

Explore how a selected paper connects to related literature.

Bring a PDF URL to read and summarize it's full text.

Read + summarize full text articles.

Identify information gaps in existing literature.

Ask questions about papers and receive thoughtful, fact-based answers.

• Next: AI Research Aids >>
• Last Updated: Mar 8, 2024 3:32 PM
• URL: https://guides.pnw.edu/AISearch

Best AI Prompts for Literature Review

Elevate your literature review game with these AI prompts from ClickUp. Dive deeper, analyze smarter, and create impactful research with the help of ClickUp AI.

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Benefits of AI for Literature Review

Unlock the power of AI to revolutionize your literature review process, saving time and enhancing the quality of your research:

• Accelerate data collection and analysis with AI-powered algorithms, allowing you to gather and process vast amounts of information in a fraction of the time.
• Improve the accuracy of your literature review by leveraging AI's natural language processing capabilities, ensuring that you don't miss any important insights or sources.
• Enhance the organization and structure of your literature review with AI-generated summaries and key findings, making it easier to navigate and understand.
• Discover hidden patterns and connections in the literature through AI-driven data mining, enabling you to identify new research gaps and opportunities.
• Stay up-to-date with the latest research in your field by leveraging AI's ability to continuously monitor and recommend relevant articles and publications.
• Collaborate seamlessly with colleagues and peers by using AI-powered tools that facilitate real-time feedback, annotation, and discussion within your literature review.

What is ClickUp Brain?

ClickUp Brain is the world's first neural network connecting tasks, docs, people, and all of your company’s knowledge with AI. It’s a knowledge manager, project manager, and writer tailored for the way you work. Use it to Generate diagrams

More than 143,000 customers revolutionize their work with ClickUp AI Brain. Boost your team's productivity by 30%, improve alignment across teams, and cut costs by up to 75%.

Teams can save time and stay focused with fewer meetings, quick summaries, and automated tasks. In fact, we find mid-market companies save around $94K per year after cutting unnecessary spend on other AI tools. People across the entire organization feel significantly more connected and aligned on their shared goals.

The days of asking a human are over. ClickUp Brain gives instant, accurate answers based on context from any work within and connected to ClickUp.

Mike Coombe MCM Agency

With the addition of ClickUp AI, I'm more efficient than ever! It saves me 3x the amount of time spent previously on Project Management tasks. Not only has it enhanced my productivity, but it has also ignited my creativity.

Best Prompts To Try for Literature Review

Prompt 1: generate a list of relevant academic sources on [insert topic] for a comprehensive literature review..

Let ClickUp AI do the heavy lifting for your literature review! By providing the topic you're researching, our AI can quickly generate a curated list of academic sources that will help you build a solid foundation for your literature review.

Prompt 2: Summarize the key findings and arguments from [insert number] scholarly articles on [insert topic] to support my literature review.

Save time and effort by letting ClickUp AI summarize multiple scholarly articles for you. Just provide the number of articles you want to include and the topic you're focusing on, and our AI will extract the most important findings and arguments, giving your literature review a strong backbone.

Prompt 3: Analyze the existing literature on [insert topic] and identify any gaps or areas for further research.

Uncover the gaps in existing literature with ClickUp AI! Our powerful language model can analyze the current research on your topic of interest and highlight areas where further research is needed. This will not only enhance the depth of your literature review but also provide valuable insights for future studies.

Prompt 4: Provide a critical analysis of the methodologies used in [insert number] research papers on [insert topic] to evaluate their reliability and validity.

Ensure the credibility of your literature review with ClickUp AI's critical analysis. Simply specify the number of research papers you want to assess and the topic you're examining, and our AI will evaluate the methodologies used in those papers, helping you determine their reliability and validity. This will strengthen the overall quality of your literature review.

[Please provide the specific topic and number of articles/papers for the AI to generate accurate results.]

AI Prompt FAQs for Literature Review

What are some ai-powered tools that can assist in generating literature review prompts.

Some AI-powered tools that can assist in generating literature review prompts include GPT-3, ChatGPT, and OpenAI's Scholar. These tools utilize natural language processing and machine learning algorithms to analyze large amounts of text data and generate relevant prompts for literature reviews. They can help researchers identify key themes, research gaps, and potential research questions based on the existing literature in a specific field. By using AI-powered tools, researchers can save time and effort in the literature review process and gain valuable insights for their research projects.

How can AI prompts enhance the efficiency and effectiveness of conducting a literature review?

AI prompts can enhance the efficiency and effectiveness of conducting a literature review by providing relevant and targeted suggestions for research topics, keywords, and relevant sources. They can help researchers explore different angles and perspectives, identify gaps in existing literature, and discover new insights. AI prompts can also assist in summarizing and synthesizing large volumes of research papers, extracting key findings and themes, and generating structured outlines for organizing the literature review. Additionally, AI prompts can recommend related articles, authors, and research groups based on user preferences and previous interactions, enabling researchers to stay up-to-date with the latest developments in their field.

Are there any specific features or capabilities that an AI tool should have in order to support literature review tasks effectively?

Yes, an AI tool for literature review tasks should ideally have features such as natural language processing, text mining, and information retrieval capabilities. These features allow the tool to analyze and extract relevant information from a large volume of literature, identify key concepts, and detect patterns or trends. Additionally, an AI tool should have advanced search and filtering options, citation management functionality, and the ability to generate summaries or abstracts of research articles. Integration with databases and academic libraries can also enhance the tool's effectiveness in supporting literature review tasks.

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Ai-assisted literature reviews.

ChatGPT has a reputation for generating hallucinations, or false information. Can an Artificial Intelligence (AI) platform be trusted to assist in a literature review? Yes, if the tool you're using is the right one for the job. ChatGPT and Copilot are not designed to provide accurate citations. Instead, use them to brainstorm research questions. Keep alert for misinformation, hallucinations, and bias that could be part of the generative AI’s responses. Be aware of historical biases in the literature, which can also influence the output you encounter. 

As with any emerging technology, the capabilities and ethical considerations of AI platforms are continually evolving, and users should stay informed about best practices and guidelines for their responsible use. These platforms may have limitations and users should carefully evaluate the quality and relevance of the sources they recommend. Be aware that these platforms have not undergone the University of Iowa’s technology review process . Users should carefully read and understand the terms of service and privacy policies before creating an account or using these tools. Finally, it is always important to avoid sharing sensitive or confidential information with any AI platform. 

Be sure to keep track of what tools you use, your purpose for using them, and the output from your interactions. Be prepared to disclose the AI tools, databases, and criteria used to select and analyze sources. Remember you are the one ultimately responsible for anything you create, generative AI is only your assistant.  

Try these five AI platforms to assist you in your literature reviews and academic research: 

• Copilot . Many people are exploring the ways that AI can be used to improve research. Even with a general generative AI platform like Copilot, you can use AI to help you brainstorm or discover new perspectives on research topics. An example prompt for this purpose can be found in David Maslach's article, " Generative AI Can Supercharge Your Academic Research ": “I am thinking about [insert topic], but this is not a very novel idea. Can you help me find innovative papers and research from the last 10 years that has discussed [insert topic]?”  
• Elicit . This AI research assistant helps in evidence synthesis and text extraction. You can enter a research question, and the AI identifies top papers in the field, even without perfect keyword matching. Elicit only includes academic papers, since Elicit is designed around finding and analyzing academic papers specifically. Elicit pulls from over 126 million papers through Semantic Scholar. Elicit organizes papers into an easy-to-use table and provides features for brainstorming research questions. 
• Consensus . This is an AI-powered search engine that pulls answers from research papers. Consensus is  not meant to be used to ask questions about basic facts such as, “How many people live in Europe?” or “When is the next leap year?” as there would likely not be research dedicated to investigating these subjects. Consensus is more effective with research questions on topics that have likely been studied by researchers. Yes/no questions will generate a “Consensus” from papers on the topic. Papers in Consensus also are from Semantic Scholar. Results in a Consensus search can be filtered by sample size of the study, population studied, study types, and more. This makes Consensus an interesting tool for finding related literature on your search topic. 
• Research Rabbit . An AI research assistant designed to assist you in literature research, discovering and organizing academic papers efficiently. It offers features such as interactive visualizations, collaborative exploration, and personalized recommendations. You can create collections of papers, visualize networks of papers and co-authorships, and explore research questions. Unlike the previous two platforms listed, Research Rabbit doesn’t start with a question, but a paper that already is known. You need to have a starting article to go down a “rabbit hole” to see connections between papers. 
• Litmaps . A similar tool to Research Rabbit, a Litmap shows the relationships between the articles in your collection in the form of connecting lines, which trace the citations for you. It allows a user to start with a citation, or a seed, and then through a simple interface, investigate connections between papers. 

For further reading, see " How to Write AI-Powered Literature Reviews: Balancing Speed, Depth, and Breadth in Academic Research ," which includes a helpful table comparing the different tools that specialize in literature searching. And check out the February 2024 webinar, " Unlock the Power of AI for Academic Research ," hosted by Tracy Mendolia-Moore and Brett Christie for more information on this topic. 

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Title: generative ai for architectural design: a literature review.

Abstract: Generative Artificial Intelligence (AI) has pioneered new methodological paradigms in architectural design, significantly expanding the innovative potential and efficiency of the design process. This paper explores the extensive applications of generative AI technologies in architectural design, a trend that has benefited from the rapid development of deep generative models. This article provides a comprehensive review of the basic principles of generative AI and large-scale models and highlights the applications in the generation of 2D images, videos, and 3D models. In addition, by reviewing the latest literature from 2020, this paper scrutinizes the impact of generative AI technologies at different stages of architectural design, from generating initial architectural 3D forms to producing final architectural imagery. The marked trend of research growth indicates an increasing inclination within the architectural design community towards embracing generative AI, thereby catalyzing a shared enthusiasm for research. These research cases and methodologies have not only proven to enhance efficiency and innovation significantly but have also posed challenges to the conventional boundaries of architectural creativity. Finally, we point out new directions for design innovation and articulate fresh trajectories for applying generative AI in the architectural domain. This article provides the first comprehensive literature review about generative AI for architectural design, and we believe this work can facilitate more research work on this significant topic in architecture.

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Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Dual MPO/PR3 ANCA positivity and vasculitis: insights from a 7-cases study and an AI-powered literature review

Affiliations.

• 1 Immunology and immunotherapy laboratory, CHU de Brest, Brest, France.
• 2 LBAI Inserm UMR 1227, Univ Brest, Brest, France.
• 3 Medical biology laboratory, Morlaix Hospital Centre, Morlaix, France.
• 4 Rheumatology department, CHU de Brest, Brest, France.
• 5 Nephrology department, CHU de Brest, Brest, France.
• 6 GGB Inserm UMR 1078, Univ Brest, Brest, France.
• 7 Internal Medicine Department, CHU de Brest, Brest, France.
• 8 GETBO Inserm UMR 1304, Univ Brest, Brest, France.
• 9 Internal Medicine Department, Morlaix Hospital Centre, Morlaix, France.
• PMID: 38552316
• DOI: 10.1093/rheumatology/keae170

Objectives: Anti-neutrophil cytoplasm antibodies (ANCA)-associated vasculitides (AAV) are rare conditions characterized by inflammatory cell infiltration in small blood vessels, leading to tissue necrosis. While most patients with AAV present antibodies against either myeloperoxidase (MPO) or proteinase 3 (PR3), rare cases of dual positivity for both antibodies (DP-ANCA) have been reported, and their impact on the clinical picture remains unclear. The goal of this study was to investigate the clinical implications, phenotypic profiles, and outcomes of patients with DP-ANCA.

Methods: A retrospective screening for DP-ANCA cases was conducted at Brest University Hospital's immunology laboratory (France), analyzing ANCA results from March 2013 to March 2022. Clinical, biological, imaging, and histological data were collected for each DP-ANCA case. Additionally, a comprehensive literature review on DP-ANCA was performed, combining an AI-based search using BIBOT software with a manual PUBMED database search.

Results: The report of our cases over the last 9 years and those from the literature yielded 103 described cases of patients with DP-ANCA. We identified four distinct phenotypic profiles: (i) idiopathic AAV (∼30%), (ii) drug-induced AAV (∼25%), (iii) autoimmune disease associated with a low risk of developing vasculitis (∼20%), and (iv) immune-disrupting comorbidities (infections, cancers, etc) not associated with AAV (∼25%).

Conclusion: This analysis of over a hundred DP-ANCA cases suggests substantial diversity in clinical and immunopathological presentations. Approximatively 50% of DP-ANCA patients develop AAV, either as drug-induced or idiopathic forms, while the remaining 50%, characterized by pre-existing dysimmune conditions, demonstrates a remarkably low vasculitis risk. These findings underscore the complex nature of DP-ANCA, its variable impact on patient health, and the necessity for personalized diagnostic and management approaches in these cases.

Keywords: ANCA-associated vasculitis; MPO; PR3; double positivity.

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