Answering real-world clinical questions using large language model based systems

• URL: http://arxiv.org/abs/2407.00541v1
• Date: Sat, 29 Jun 2024 22:39:20 GMT
• Title: Answering real-world clinical questions using large language model based systems
• Authors: Yen Sia Low, Michael L. Jackson, Rebecca J. Hyde, Robert E. Brown, Neil M. Sanghavi, Julian D. Baldwin, C. William Pike, Jananee Muralidharan, Gavin Hui, Natasha Alexander, Hadeel Hassan, Rahul V. Nene, Morgan Pike, Courtney J. Pokrzywa, Shivam Vedak, Adam Paul Yan, Dong-han Yao, Amy R. Zipursky, Christina Dinh, Philip Ballentine, Dan C. Derieg, Vladimir Polony, Rehan N. Chawdry, Jordan Davies, Brigham B. Hyde, Nigam H. Shah, Saurabh Gombar,
• Abstract summary: Large language models (LLMs) could potentially address both challenges by either summarizing published literature or generating new studies based on real-world data (RWD) We evaluated the ability of five LLM-based systems in answering 50 clinical questions and had nine independent physicians review the responses for relevance, reliability, and actionability.
• Score: 2.2605659089865355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evidence to guide healthcare decisions is often limited by a lack of relevant and trustworthy literature as well as difficulty in contextualizing existing research for a specific patient. Large language models (LLMs) could potentially address both challenges by either summarizing published literature or generating new studies based on real-world data (RWD). We evaluated the ability of five LLM-based systems in answering 50 clinical questions and had nine independent physicians review the responses for relevance, reliability, and actionability. As it stands, general-purpose LLMs (ChatGPT-4, Claude 3 Opus, Gemini Pro 1.5) rarely produced answers that were deemed relevant and evidence-based (2% - 10%). In contrast, retrieval augmented generation (RAG)-based and agentic LLM systems produced relevant and evidence-based answers for 24% (OpenEvidence) to 58% (ChatRWD) of questions. Only the agentic ChatRWD was able to answer novel questions compared to other LLMs (65% vs. 0-9%). These results suggest that while general-purpose LLMs should not be used as-is, a purpose-built system for evidence summarization based on RAG and one for generating novel evidence working synergistically would improve availability of pertinent evidence for patient care.

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