Background Large language model (LLM) chatbots demonstrate high degrees of accuracy, yet recent studies found that physicians using these same chatbots may score no better to worse on clinical reasoning tests compared to the chatbot performing alone with researcher-curated prompts. It is unknown how physicians approach inputting information into chatbots.

Objective This study aimed to identify how physicians interacted with LLM chatbots on clinical reasoning tasks to create a typology of input approaches, exploring whether input approach type was associated with improved clinical reasoning performance.

Methods We carried out a mixed methods study in three steps. First, we conducted semi-structured interviews with U.S. physicians on experiences using an LLM chatbot and analyzed transcripts using the Framework Method to develop a typology based on input patterns. Next, we analyzed the chat logs of physicians who used a chatbot while solving clinical cases, categorizing each case to an input approach type. Lastly, we used a linear mixed-effects model to compare each input approach type with performance on the clinical cases.

Results We identified four input approach types based on patterns of “content amount”: copy-paster (entire case), selective copy-paster (pieces of a case), summarizer (user-generated case summary), and searcher (short queries). Copy-pasting and searching were utilized most. No single type was associated with scoring higher on clinical cases.

Discussion This study adds to our understanding of how physicians approach using chatbots and identifies ways in which physicians intuitively interact with chatbots.

Conclusions Purposeful training and support is needed to help physicians effectively use emerging AI technologies and realize their potential for supporting safe and effective medical decision-making in practice.

Jason Hom reports Being an advisor for Cognita Imaging and having equity options Jonathan Chen has received additional research funding support in part by NIH/National Institute of Allergy and Infectious Diseases (1R01AI17812101) NIH-NCATS-Clinical & Translational Science Award (UM1TR004921) NIH/National Institute on Drug Abuse Clinical Trials Network (UG1DA015815 - CTN-0136) Stanford Bio-X Interdisciplinary Initiatives Seed Grants Program (IIP) [R12] [JHC] NIH/Center for Undiagnosed Diseases at Stanford (U01 NS134358) Stanford Institute for Human-Centered Artificial Intelligence (HAI) Jonathan Chen reports being Co-founder of Reaction Explorer LLC that develops and licenses organic chemistry education software. Paid medical expert witness fees from Sutton Pierce, Younker Hyde MacFarlane, Sykes McAllister, and Elite Experts. Paid consulting fees from ISHI Health. Paid honoraria or travel expenses for invited presentations by General Reinsurance Corporation, Cozeva, and other industry conferences, academic institutions, and health systems. Dr Rodman reports additional research funding paid in part by: Josiah Jr. Macy Foundation (P25-04) Dr. Rodman reports being A part-time visiting researcher at Google

This study was funded by the Gordon and Betty Moore Foundation (Grant #12409) and the Stanford Department of Medicine-Improvement Capability Development Program.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study underwent an exempt review by the Stanford Institutional Review Board, eProtocol #73932, and Beth Israel Deaconess Medical Center, Protocol #2024P000074.

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