Use of generative artificial intelligence in medical libraries: A scoping review
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Abstract
Purpose. This study aims to map how health and medical libraries have used GenAI to support library services.
Methodology. Scoping review following PRISMA-ScR. Searches were conducted between 2020 and 2025 in PubMed, Embase, CINAHL, LISTA, Academic Search Premier, Ovid, Scopus, Web of Science, ProQuest Dissertations & Theses, and Google Scholar. Of 250 records, 106 remained after deduplication to screening in Rayyan, blinded, by two reviewers.
Results and discussion. We included 11 studies: 7 primary and 4 secondary/methods. Among primaries, 5 directly used GPT-family models (GPT-4/ChatGPT/GPT-4o or Perplexity layered on GPT-4) to: answer clinical questions, support search-strategy design, trial library tasks, and integrate source-linked answers in a national portal. One survey assessed intention to adopt generative chatbots; one case described a rule-based chatbot. GenAI accelerates workflows and extends services but requires human verification, transparency about limitations, privacy policies, and staff training (including prompt engineering). LLM-generated search strategies showed variability and underperformed human designs.
Conclusions. In its current state, GenAI works best as a copilot useful for drafting, triage, and initial sense-making, while librarians remain essential for high-precision tasks (performance), quality control (reliability), and governance.
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References
Adegboye, M., Vaidhyam, S., & Huang, K.-T. (2024). Generative AI-ChatGPT’s Impact in Health Science Libraries. Proceedings of the ALISE Annual Conference. https://doi.org/10.21900/j.alise.2024.1659
Blasingame, M. N., Koonce, T. Y., Williams, A. M., Giuse, D. A., Su, J., Krump, P. A., & Giuse, N. B. (2025). Evaluating a large language model’s ability to answer clinicians’ requests for evidence summaries. Journal of the Medical Library Association : JMLA, 113(1), 65–77. https://doi.org/10.5195/jmla.2025.1985
Bourgeois, J. P., & Ellingson, H. (2025). Ability of ChatGPT to Generate Systematic Review Search Strategies Compared to a Published Search Strategy. Medical Reference Services Quarterly, 44(3), 279–291. https://doi.org/10.1080/02763869.2025.2537075
Cunha, M. B. da. (2025). Bibliografia sobre o uso da inteligência artificial na biblioteca. RDBCI: Revista Digital de Biblioteconomia e Ciência da Informação, 24(00), e026003. https://doi.org/10.20396/rdbci.v24i00.8680239
Formanek, M. (2025). Exploring the potential of large language models and generative artificial intelligence (GPT): Applications in Library and Information Science. Journal of Librarianship and Information Science, 57(2), 568–590. https://doi.org/10.1177/09610006241241066
García-Puente, M. (2025). La inteligencia artificial generativa en la biblioteca médica: Transformando el acceso al conocimiento y el rol profesional. CLIP de SEDIC: Revista de la Sociedad Española de Documentación e Información Científica, (91), 1–8. https://doi.org/10.47251/clip.n91.163
Lund, B. D., Khan, D., & Yuvaraj, M. (2024). ChatGPT in medical libraries, possibilities and future directions: An integrative review. Health Information & Libraries Journal, 41(1), 4–15. https://doi.org/10.1111/hir.12518
Oluchi Emmanuel, V., Peter Ameh, M., & Oladokun, B. D. (2025). Generative Chatbots in the Era of Library 5.0: A Dilemma for Libraries? Metaverse Basic and Applied Research, 4, 157. https://doi.org/10.56294/mr2025157
Orubebe, E. D., Ijaja, E. M., Ogwula, J. A., & Oladokun, B. D. (2024). Transforming Medical Libraries: Opportunities, Challenges, and Strategies for Integrating Artificial Intelligence. Asian Journal of Information Science and Technology, 14(2), 82–87. https://doi.org/10.70112/ajist-2024.14.2.4298
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
Picalho, A. C., de Oliveira, G. R., & Cativelli, A. S. (2025). Artificial intelligence in bibliographic searches in scientific databases: Comparing search expressions in ChatGPT, Copilot, and Gemini. Revista Digital de Biblioteconomia e Ciencia da Informacao, 23. Scopus. https://doi.org/10.20396/rdbci.v23i00.8678378
Robinson, K., Bontekoe, K., & Muellenbach, J. (2025). Integrating PICO principles into generative artificial intelligence prompt engineering to enhance information retrieval for medical librarians. Journal of the Medical Library Association : JMLA, 113(2), 184–188. https://doi.org/10.5195/jmla.2025.2022
Schweizer, S., Vogel, C., & Weiner, O. (2023). Can we use artificial intelligence like ChatGPT productively for medical library work? GMS Medizin-Bibliothek-Information, 23(1), 1–10. https://doi.org/10.3205/mbi000554
Sen, S. (2025). AI and generative AI in health and medical libraries: A scoping review of present use and emerging potential. Journal of EAHIL, 21(2). https://doi.org/10.32384/jeahil21675
Song, B., & Park, S. (2023). Analyzing Influencing Factors in Medical Library Librarians’ Intentions to Adopt Generative AI Chatbot Services: A Focus on the Extended Technology Acceptance Model. Journal of Korean Medical Library Association, 50(1_2), 54–64. https://doi.org/10.69528/jkmla.2023.50.1_2.54
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
Wang, J., & Moody, H. (2025). Language inclusion intentions in scoping reviews. Journal of the Medical Library Association, 113(4), 290–297. https://doi.org/10.5195/jmla.2025.2170
Эргашева, Л. (2024). ОПЫТ РАЗРАБОТКИ И ВНЕДРЕНИЯ МЕДИЦИНСКОЙ ПОИСКОВОЙ СИСТЕМЫ TESSERAСТ ГНМБ. Information Library Magazine “INFOLIB”, (2), 70–73. https://doi.org/10.34920/2181-8207/2024/2-125