ArticleBMC medical education2025
Teaching opportunities for anamnesis interviews through AI based teaching role plays: a survey with online learning students from health study programs.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Large Language Model-Based Virtual Patient Systems for History-Taking in Medical Education: Comprehensive Systematic Review.JMIR medical informatics · 2026Pooled it
- Artificial intelligence (AI) for social innovation in health education: promoting health literacy through personalized ai-driven learning tools - a systematic review.BMC medical education · 2025Pooled it
- Application of an AI-enabled personalized medical education model for improving clinical thinking among cardiovascular surgery trainees.BMC medical education · 2026Trial
- An exploratory study of the use of artificial intelligence-based virtual patients to enhance dentist-patient communication training.BMC medical education · 2026Trial
- Development and pilot evaluation of a large language model-based clinical simulation system for medical education in gastroenterology.BMC medical education · 2026Article
- Feasibility of Large Language Model-Based Standardized Virtual Patients to Support Clinical Decision-Making Training in Operative Dentistry: Mixed Methods Study.JMIR formative research · 2026Article
- Artificial Intelligence Chatbots as Virtual Patients in Dental Education: A Constructivist Approach to Classroom Implementation.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Article
- Safety-First Framework for AI-Enabled Anamnesis in Head and Neck Surgery: Evidence Synthesis from a Narrative Review.Journal of clinical medicine · 2026Review
- Enhancing history-taking education through GPT-4-based virtual patients and automated assessment: a study of medical student perceptions.Korean journal of medical education · 2026Article
- Development and validation of a competency-based evaluation framework for clinical medical trainees in the era of artificial intelligence: a mixed-methods study in China.BMC medical education · 2026Article
- Mapping applications and evaluations of LLM-enabled AI chatbots for health purposes: a scoping review.Frontiers in public health · 2026Article
- AI-assisted learning in exercise physiology: a quasi-experimental study using PhysioExercise GPT.Frontiers in physiology · 2026Article
- Embracing the Future of Medical Education With Large Language Model-Based Virtual Patients: Scoping Review.Journal of medical Internet research · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study presents a novel approach to educational role-playing through an AI-based bot, leveraging GPT-4 to simulate anamnesis interviews in various learning scenarios. Developed collaboratively by an interdisciplinary team of university lecturers and AI experts, the bot provides a platform for students of different health study programs to engage in complex patient-health professional conversations, offering an alternative to traditional role plays with actors or real patients.
methodsThis study utilized a GPT-4 based digital teaching assistant, implemented through a proprietary chatbot design platform, to train anamnesis interviews in virtual settings with students from different online health care study programs. Students' satisfaction, virtual patient's accuracy, its realism, and quality were evaluated with a quantitative survey.
resultsThe evaluation of the bot focused on student feedback, highlighting a preference for the AI-driven method due to its immersive and interactive nature. Preliminary results show that students consistently rate the language ability of the AI model positively. More than 80% of students rated the professional and content-related precision of the virtual patient as good to excellent. Even as a text-based chatbot, the vast majority of students see a fairly close to very close relationship to a real anamnesis interview. The results further indicate that students even prefer this training approach to traditional in-person role-plays.
conclusionsThe study underscores the bot's potential as a versatile tool for enriching learning experiences across multiple health disciplines, signaling a meaningful shift in educational practices towards the integration of AI technologies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.