Librarians & Clinical AI Tools Talk:
AI can do that – or can it? Identifying best (and worst) AI use cases in the EBDM process
Over the past few years, generative artificial intelligence (AI) has gone from novelty to emerging technology to a core piece of our day-to-day lives. As this has happened, researchers in the health sciences have assessed the ability of generative artificial intelligence (AI) models to perform multiple tasks in the evidence cycle, from literature searching to summarizing articles for clinical applications to augmenting human reviewers in assessing risk of bias. For health sciences librarians, the topic of AI is complex and sometimes fraught. Can we trust AI as a source of reliable information and, if so, where does that leave us? Join OHSLA for a deep dive into AI and the evidence cycle as we explore its strengths, weaknesses, and our place within the new information framework.
Presenter Bio:
Laura Menard, MLS, RTIF graduated from the University of North Carolina at Chapel Hill with her MLS in 2011. She has fifteen years of experience working as a librarian and educator in the health sciences. She is an award-winning researcher with a focus on medical education, AI, and assessment, and is active in the Medical Library Association's Research Training Institute as well as the Association of Academic Health Sciences Libraries task force on educational research and assessment.
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