ArticleComputational and structural biotechnology journal2025
Integrating large language model-based agents into a virtual patient chatbot for clinical anamnesis training.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Usability and User Experience Assessment of Health Care Conversational Agents Using Validated Subjective Instruments: Systematic Review and Comparative Analysis.JMIR human factors · 2026Pooled it
- Large Language Model-Based Virtual Patient Systems for History-Taking in Medical Education: Comprehensive Systematic Review.JMIR medical informatics · 2026Pooled it
- Synthetic relationships with Social Pedagogical Agents in education: a scoping literature review.Frontiers in artificial intelligence · 2026Pooled it
- Application of an AI-enabled personalized medical education model for improving clinical thinking among cardiovascular surgery trainees.BMC medical education · 2026Trial
- 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
- Review
- Safety-First Framework for AI-Enabled Anamnesis in Head and Neck Surgery: Evidence Synthesis from a Narrative Review.Journal of clinical medicine · 2026Review
- Virtual patients and standardized patients combined training is associated with improved clinical reasoning among medical students.Frontiers in medicine · 2026Article
- The digital transformation of clinical teaching: a review of AI and data-driven quality assurance systems in medical education.Frontiers in public health · 2026Review
- Artificial intelligence in undergraduate medical education clinical skills curricula: a scoping review of implementations since 2022.Frontiers in digital health · 2026Review
- Large language model-driven agents in nursing practice: A scoping review.International journal of nursing sciences · 2025Review
- Artificial Intelligence and Medical Education (2013-2024): A Scopus-Based Bibliometric Analysis.The East African health research journal · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective communication is crucial for trust-building, accurate information gathering, and clinical decision-making in healthcare. Despite its emphasis in medical curricula, traditional training methods, such as role-playing with standardized patients, remain costly, logistically complex, and fail to replicate real-life scenarios. Simulation-based training enhances communication and reasoning skills, but novice learners often struggle due to underdeveloped reasoning processes. Furthermore, limited access to asynchronous, autonomous simulated patient interactions restricts personalized practice. Virtual patient models offer scalable solutions with interactive scenarios and tailored feedback, but high development costs and resource demands hinder their widespread adoption. To address these challenges, virtual patient systems powered by Large Language Models (LLMs) have emerged as a promising tool. These generative agents simulate human-like behavioral responses by leveraging LLM capabilities, cognitive mechanisms, and contextual memory retrieval. A tool was developed allowing students to select clinical cases and interact with a chatbot simulating a patient role. Teachers can also create custom cases. Evaluations showed that the agent provided consistent, plausible responses aligned with case descriptions and achieved a Chatbot Usability Questionnaire (CUQ) score of 86.25/100. Our results show that this approach enables flexible, repetitive, and asynchronous practice while offering real-time feedback.
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Registered trials
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.