Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial
Abstract Backgrounds Traditional methods of teaching history-taking in medical education are limited by scalability and resource intensity. This study aims to assess the effectiveness of simulated patient interactions based on a custom-designed Generative Pre-trained Transformer (GPT) model, develop...
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BMC
2025-07-01
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| Series: | BMC Medical Education |
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| Online Access: | https://doi.org/10.1186/s12909-025-07614-9 |
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| author | Zhen Wang Ting-Ting Fan Meng-Li Li Nin-Jun Zhu Xiao-Chen Wang |
| author_facet | Zhen Wang Ting-Ting Fan Meng-Li Li Nin-Jun Zhu Xiao-Chen Wang |
| author_sort | Zhen Wang |
| collection | DOAJ |
| description | Abstract Backgrounds Traditional methods of teaching history-taking in medical education are limited by scalability and resource intensity. This study aims to assess the effectiveness of simulated patient interactions based on a custom-designed Generative Pre-trained Transformer (GPT) model, developed using OpenAI’s ChatGPT GPTs platform, in enhancing medical students’ history-taking skills compared to traditional role-playing methods. Methods A total of 56 medical students were randomly assigned into two groups: an GPT group using GPT-simulated patients and a control group using traditional role-playing. Pre- and post-training assessments were conducted using a structured clinical examination to measure students’ abilities in history collection, clinical reasoning, communication skills, and professional behavior. Additionally, students’ evaluations of the educational effectiveness, satisfaction, and recommendation likelihood were assessed. Results The GPT-simulation group showed significantly higher post-training scores in the structured clinical examination compared to the control group (86.79 ± 5.46,73.64 ± 4.76, respectively, P < 0.001). Students in the GPT group exhibited higher enthusiasm for learning, greater self-directed learning motivation, and better communication feedback abilities compared to the control group (P < 0.05). Additionally, the student satisfaction survey revealed that the GPT group rated higher on the diversity of diseases encountered, ease of use, and likelihood of recommending the training compared to the control group (P < 0.05). Conclusions GPT-based history-taking training effectively enhances medical students’ history-taking skills, providing a solid foundation for the application of artificial intelligence (AI) in medical education. Clinical trial number NCT06766383. |
| format | Article |
| id | doaj-art-4b1a30873165465f82dfec5442fd1a2f |
| institution | Kabale University |
| issn | 1472-6920 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Education |
| spelling | doaj-art-4b1a30873165465f82dfec5442fd1a2f2025-08-20T03:42:51ZengBMCBMC Medical Education1472-69202025-07-0125111110.1186/s12909-025-07614-9Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trialZhen Wang0Ting-Ting Fan1Meng-Li Li2Nin-Jun Zhu3Xiao-Chen Wang4Department of Cardiology, The Second Affiliated Hospital of Anhui Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Anhui Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Anhui Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Anhui Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Anhui Medical UniversityAbstract Backgrounds Traditional methods of teaching history-taking in medical education are limited by scalability and resource intensity. This study aims to assess the effectiveness of simulated patient interactions based on a custom-designed Generative Pre-trained Transformer (GPT) model, developed using OpenAI’s ChatGPT GPTs platform, in enhancing medical students’ history-taking skills compared to traditional role-playing methods. Methods A total of 56 medical students were randomly assigned into two groups: an GPT group using GPT-simulated patients and a control group using traditional role-playing. Pre- and post-training assessments were conducted using a structured clinical examination to measure students’ abilities in history collection, clinical reasoning, communication skills, and professional behavior. Additionally, students’ evaluations of the educational effectiveness, satisfaction, and recommendation likelihood were assessed. Results The GPT-simulation group showed significantly higher post-training scores in the structured clinical examination compared to the control group (86.79 ± 5.46,73.64 ± 4.76, respectively, P < 0.001). Students in the GPT group exhibited higher enthusiasm for learning, greater self-directed learning motivation, and better communication feedback abilities compared to the control group (P < 0.05). Additionally, the student satisfaction survey revealed that the GPT group rated higher on the diversity of diseases encountered, ease of use, and likelihood of recommending the training compared to the control group (P < 0.05). Conclusions GPT-based history-taking training effectively enhances medical students’ history-taking skills, providing a solid foundation for the application of artificial intelligence (AI) in medical education. Clinical trial number NCT06766383.https://doi.org/10.1186/s12909-025-07614-9Artificial IntelligenceHistory-taking TrainingMedical EducationGPT SimulationClinical Skills |
| spellingShingle | Zhen Wang Ting-Ting Fan Meng-Li Li Nin-Jun Zhu Xiao-Chen Wang Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial BMC Medical Education Artificial Intelligence History-taking Training Medical Education GPT Simulation Clinical Skills |
| title | Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial |
| title_full | Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial |
| title_fullStr | Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial |
| title_full_unstemmed | Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial |
| title_short | Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial |
| title_sort | feasibility study of using gpt for history taking training in medical education a randomized clinical trial |
| topic | Artificial Intelligence History-taking Training Medical Education GPT Simulation Clinical Skills |
| url | https://doi.org/10.1186/s12909-025-07614-9 |
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