Application of Large Language Models in Medical Interview Training: A Study with Medical Students

Application of Large Language Models in Medical Interview Training: A Study with Medical Students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application of Large Language Models in Medical Interview Training: A Study with Medical Students Artur Michałek, Jarosław Hryszko, Adam Roman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6569758/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2026 Read the published version in BMC Medical Education → Version 1 posted 22 You are reading this latest preprint version Abstract This paper explores the application of Large Language Models (LLMs) in medical interview training. While medical interviews remain fundamental in healthcare, training methods often require human interaction, limiting practice opportunities. We investigate if LLMs can effectively simulate patients for training purposes. We examined commercially available models and fine-tuned open-source LLMs using QLoRA techniques on a dataset of medical interviews. We developed a web application employing these models and conducted in-depth interviews with medical students to evaluate its effectiveness. Students found the application helpful, rating conversation quality as good and highlighting advantages over traditional training methods, particularly regarding availability and consistency in patient symptom presentation. While students emphasized that LLMs cannot replace real patient interactions, they recognized significant benefits for supplementary training. Our findings confirm that LLMs can be valuable tools in medical interview training, providing opportunities for skill development without dependency on peer availability or scheduled sessions. Large Language Models Medical Education Virtual Patients Medical Interviews Healthcare Simulation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2026 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 24 Nov, 2025 Reviews received at journal 22 Nov, 2025 Reviews received at journal 21 Nov, 2025 Reviews received at journal 20 Nov, 2025 Reviews received at journal 19 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviews received at journal 15 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor invited by journal 10 Nov, 2025 Editor assigned by journal 07 Nov, 2025 Submission checks completed at journal 07 Nov, 2025 First submitted to journal 01 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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