Making the most of Artificial Intelligence and Large Language Models to support collection development in health sciences libraries.
This project investigated the potential of generative AI models in aiding health sciences librarians with collection development. Researchers at Chapman University's Harry and Diane Rinker Health Science campus evaluated four generative AI models--ChatGPT 4.0, Google Gemini, Perplexity, and Microsof...
| Publicado en: | Journal of the Medical Library Association Vol. 113; no. 1; pp. 92 - 94 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
University of Pittsburgh, University Library System
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182552277&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182552277 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15365050 PI8 jtl: Journal of the Medical Library Association issn: 15365050 maglogo: N pubinfo: dt: Jan2025 vid: 113 iid: 1 pid: 60406 pub: University of Pittsburgh, University Library System place: Pittsburgh, Pennsylvania artinfo: ui: 182552277 10.5195/jmla.2025.2079 182552277 ppf: 92 ppct: 2 formats: fmt: @attributes: type: P tig: atl: Making the most of Artificial Intelligence and Large Language Models to support collection development in health sciences libraries. aug: au: Portillo, Ivan Carson, David affil: Health Sciences Librarian, Director of Rinker Campus Library Services, Leatherby Libraries, Chapman University, Irvine, CA sug: subj: Libraries, Health Sciences Collection Development Artificial Intelligence Models, Educational Artificial Intelligence, Generative Librarianship Technology Communication Information Science Pharmacy Service ab: This project investigated the potential of generative AI models in aiding health sciences librarians with collection development. Researchers at Chapman University's Harry and Diane Rinker Health Science campus evaluated four generative AI models--ChatGPT 4.0, Google Gemini, Perplexity, and Microsoft Copilot--over six months starting in March 2024. Two prompts were used: one to generate recent eBook titles in specific health sciences fields and another to identify subject gaps in the existing collection. The first prompt revealed inconsistencies across models, with Copilot and Perplexity providing sources but also inaccuracies. The second prompt yielded more useful results, with all models offering helpful analysis and accurate Library of Congress call numbers. The findings suggest that Large Language Models (LLMs) are not yet reliable as primary tools for collection development due to inaccuracies and hallucinations. However, they can serve as supplementary tools for analyzing subject coverage and identifying gaps in health sciences collections. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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