The feasibility of using generative artificial intelligence for history taking in virtual patients
Open accessAbstract
OBJECTIVE: This study aimed to design and develop a virtual patient program using generative Artificial Intelligence (AI) technology, providing medical students opportunities to practice history-taking with a chatbot. We evaluated the feasibility of this approach by analyzing the quality of responses generated by the chatbot. RESULTS: . They evaluated the AI responses using a five-item questionnaire rated on a five-point Likert scale. The chatbot generated 96 pairs of questions and answers, totaling 1,325 words in 177 sentences. Discourse analysis of the scripts revealed that 2.6% (34) of the words generated by the chatbot were deemed implausible and were categorized into inarticulate answers, hallucinations, and missing important information. Participants rated the AI answers as relevant (M = 4.50 ± 0.32), valid (M = 4.20 ± 0.40), accurate (M = 4.10 ± 0.20), and succinct (M = 3.80 ± 0.51), but were neutral about their fluency (M = 3.20 ± 0.60). Using generative AI for history-taking of virtual patients is feasible, but improvements are needed for more articulate and natural responses.
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Early educational research suggests that AI-generated virtual patients may improve learners' engagement, provide unlimited practice opportunities, and support consistent exposure to rare or sensitive clinical presentations.
“Early educational research suggests that AI-generated virtual patients may improve learners’ engagement [8, 9], provide unlimited practice opportunities [10–12], and support consistent exposure to rare or sensitive clinical presentations.”
From Utilizing generative artificial intelligence to create simulated patient for history taking in gynecology (2026) · cites this paper as [10]
The Discussion says the AI-generated virtual patient could give students additional opportunities to practice history taking without relying on standardized or real patients, supporting the practice-opportunities point. It does not call those opportunities unlimited. The Limitations section says the prototype covered only one urinary presentation and had not yet been tested with students, so it does not establish improved learner engagement or consistent exposure to rare or sensitive presentations. (LLM, conf=0.86)
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“23 Few have incorporated analyses of dialogue realism and authenticity.”
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“Early educational research suggests that AI-generated virtual patients may improve learners’ engagement [8, 9], provide unlimited practice opportunities [10–12], and support consistent exposure to rare or sensitive clinical presentations.”
- Authors
- Yi, Y., & Kim, K.-J.
- Affiliations
- Dankook University Hospital (KR)
- Dongguk University (KR)
- Corresponding author
- Kim, Kyong-Jee
- Venue
- BMC Research Notes
- Published
- 2025-02-24
- Volume
- 18
- Issue
- 1
- Pages
- 80-80
- ISSN
- 1756-0500 (ISSN-L)
- PMC
- PMC11849343
- OpenAlex
- W4407892103
- Cited by
- 28
- References
- 6
- Citations by year
- 2026: 16 · 2025: 12
- FWCI
- 6.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Research Notes (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Asosocation for Medical Education in Europe (TEL Innovation Development Grant 2022)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- generative AI, medical history, virtual patients, chatbots, medical students, clinical interviewing skills, hallucinations, medical education
- MeSH terms
- Virtual Reality, Generative Artificial Intelligence, Adult, Artificial Intelligence, Feasibility Studies, Female, Humans, Male, Medical History Taking, Pilot Projects, Surveys and Questionnaires, Students, Medical, User-Computer Interface
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