{"title":"通过人工智能模拟患者互动提高医学访谈技能:非随机对照试验","authors":"Akira Yamamoto, Masahide Koda, Hiroko Ogawa, Tomoko Miyoshi, Yoshinobu Maeda, Fumio Otsuka, Hideo Ino","doi":"10.2196/58753","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Medical interviewing is a critical skill in clinical practice, yet opportunities for practical training are limited in Japanese medical schools, necessitating urgent measures. Given advancements in artificial intelligence (AI) technology, its application in the medical field is expanding. However, reports on its application in medical interviews in medical education are scarce.</p><p><strong>Objective: </strong>This study aimed to investigate whether medical students' interview skills could be improved by engaging with AI-simulated patients using large language models, including the provision of feedback.</p><p><strong>Methods: </strong>This nonrandomized controlled trial was conducted with fourth-year medical students in Japan. A simulation program using large language models was provided to 35 students in the intervention group in 2023, while 110 students from 2022 who did not participate in the intervention were selected as the control group. The primary outcome was the score on the Pre-Clinical Clerkship Objective Structured Clinical Examination (pre-CC OSCE), a national standardized clinical skills examination, in medical interviewing. Secondary outcomes included surveys such as the Simulation-Based Training Quality Assurance Tool (SBT-QA10), administered at the start and end of the study.</p><p><strong>Results: </strong>The AI intervention group showed significantly higher scores on medical interviews than the control group (AI group vs control group: mean 28.1, SD 1.6 vs 27.1, SD 2.2; P=.01). There was a trend of inverse correlation between the SBT-QA10 and pre-CC OSCE scores (regression coefficient -2.0 to -2.1). No significant safety concerns were observed.</p><p><strong>Conclusions: </strong>Education through medical interviews using AI-simulated patients has demonstrated safety and a certain level of educational effectiveness. However, at present, the educational effects of this platform on nonverbal communication skills are limited, suggesting that it should be used as a supplementary tool to traditional simulation education.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459107/pdf/","citationCount":"0","resultStr":"{\"title\":\"Enhancing Medical Interview Skills Through AI-Simulated Patient Interactions: Nonrandomized Controlled Trial.\",\"authors\":\"Akira Yamamoto, Masahide Koda, Hiroko Ogawa, Tomoko Miyoshi, Yoshinobu Maeda, Fumio Otsuka, Hideo Ino\",\"doi\":\"10.2196/58753\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Medical interviewing is a critical skill in clinical practice, yet opportunities for practical training are limited in Japanese medical schools, necessitating urgent measures. Given advancements in artificial intelligence (AI) technology, its application in the medical field is expanding. However, reports on its application in medical interviews in medical education are scarce.</p><p><strong>Objective: </strong>This study aimed to investigate whether medical students' interview skills could be improved by engaging with AI-simulated patients using large language models, including the provision of feedback.</p><p><strong>Methods: </strong>This nonrandomized controlled trial was conducted with fourth-year medical students in Japan. A simulation program using large language models was provided to 35 students in the intervention group in 2023, while 110 students from 2022 who did not participate in the intervention were selected as the control group. The primary outcome was the score on the Pre-Clinical Clerkship Objective Structured Clinical Examination (pre-CC OSCE), a national standardized clinical skills examination, in medical interviewing. Secondary outcomes included surveys such as the Simulation-Based Training Quality Assurance Tool (SBT-QA10), administered at the start and end of the study.</p><p><strong>Results: </strong>The AI intervention group showed significantly higher scores on medical interviews than the control group (AI group vs control group: mean 28.1, SD 1.6 vs 27.1, SD 2.2; P=.01). There was a trend of inverse correlation between the SBT-QA10 and pre-CC OSCE scores (regression coefficient -2.0 to -2.1). No significant safety concerns were observed.</p><p><strong>Conclusions: </strong>Education through medical interviews using AI-simulated patients has demonstrated safety and a certain level of educational effectiveness. However, at present, the educational effects of this platform on nonverbal communication skills are limited, suggesting that it should be used as a supplementary tool to traditional simulation education.</p>\",\"PeriodicalId\":36236,\"journal\":{\"name\":\"JMIR Medical Education\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459107/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR Medical Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2196/58753\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION, SCIENTIFIC DISCIPLINES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/58753","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0
摘要
背景:医学访谈是临床实践中的一项关键技能,但在日本的医学院中,实践培训的机会非常有限,因此有必要采取紧急措施。随着人工智能(AI)技术的发展,其在医学领域的应用也在不断扩大。然而,有关其在医学教育中应用于医学面试的报道却很少:本研究旨在探讨通过使用大型语言模型与人工智能模拟的患者进行交流,包括提供反馈,能否提高医学生的面谈技能:这项非随机对照试验在日本的四年级医学生中进行。2023 年,干预组的 35 名学生接受了使用大型语言模型的模拟程序,而 2022 年未参与干预的 110 名学生被选为对照组。主要结果是在国家标准化临床技能考试--临床实习前客观结构化临床考试(pre-CC OSCE)中的医学访谈得分。次要结果包括在研究开始和结束时进行的模拟培训质量保证工具(SBT-QA10)等调查:结果:人工智能干预组的医学访谈得分明显高于对照组(人工智能组 vs 对照组:平均 28.1,SD 1.6 vs 27.1,SD 2.2;P=.01)。SBT-QA10与CC前OSCE评分呈反相关趋势(回归系数-2.0至-2.1)。未发现重大安全问题:结论:使用人工智能模拟病人进行医学访谈教育具有安全性和一定的教育效果。然而,目前该平台对非语言沟通技能的教育效果有限,这表明它应作为传统模拟教育的补充工具。
Enhancing Medical Interview Skills Through AI-Simulated Patient Interactions: Nonrandomized Controlled Trial.
Background: Medical interviewing is a critical skill in clinical practice, yet opportunities for practical training are limited in Japanese medical schools, necessitating urgent measures. Given advancements in artificial intelligence (AI) technology, its application in the medical field is expanding. However, reports on its application in medical interviews in medical education are scarce.
Objective: This study aimed to investigate whether medical students' interview skills could be improved by engaging with AI-simulated patients using large language models, including the provision of feedback.
Methods: This nonrandomized controlled trial was conducted with fourth-year medical students in Japan. A simulation program using large language models was provided to 35 students in the intervention group in 2023, while 110 students from 2022 who did not participate in the intervention were selected as the control group. The primary outcome was the score on the Pre-Clinical Clerkship Objective Structured Clinical Examination (pre-CC OSCE), a national standardized clinical skills examination, in medical interviewing. Secondary outcomes included surveys such as the Simulation-Based Training Quality Assurance Tool (SBT-QA10), administered at the start and end of the study.
Results: The AI intervention group showed significantly higher scores on medical interviews than the control group (AI group vs control group: mean 28.1, SD 1.6 vs 27.1, SD 2.2; P=.01). There was a trend of inverse correlation between the SBT-QA10 and pre-CC OSCE scores (regression coefficient -2.0 to -2.1). No significant safety concerns were observed.
Conclusions: Education through medical interviews using AI-simulated patients has demonstrated safety and a certain level of educational effectiveness. However, at present, the educational effects of this platform on nonverbal communication skills are limited, suggesting that it should be used as a supplementary tool to traditional simulation education.