The feasibility of using generative artificial intelligence for history taking in virtual patients.

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

BMC Research Notes Pub Date : 2025-02-24 DOI:10.1186/s13104-025-07157-8

Yongjin Yi, Kyong-Jee Kim

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引用次数: 0

Abstract

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: Five expert reviewers participated in a pilot test, interacting with the chatbot to take the history of a patient presenting with a urinary problem using the Korean AI platform Naver HyperCLOVA X^®. 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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来源期刊

BMC Research Notes Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)

CiteScore

3.60

自引率

0.00%

发文量

363

审稿时长

15 weeks

期刊介绍： BMC Research Notes publishes scientifically valid research outputs that cannot be considered as full research or methodology articles. We support the research community across all scientific and clinical disciplines by providing an open access forum for sharing data and useful information; this includes, but is not limited to, updates to previous work, additions to established methods, short publications, null results, research proposals and data management plans.