Emily Rush, Jessica N Byram, Colleen N Garnett, Nicole DeVaul, Laura Smith, Margaret Checchi, Daniel Martin, Leslie A Hoffman, Kirstin M Brown, Daniel J Mumbower, Robert M Becker, Victoria A Roach, Alison F Doubleday, Danielle N Edwards, Rebecca S Lufler, Alexandra Wactor, Sophia Boxerman, Suzanne Smith, Hannah Herriott, Adam B Wilson
{"title":"美国医学院人工智能相关文件审计：基于框架的定性内容分析。","authors":"Emily Rush, Jessica N Byram, Colleen N Garnett, Nicole DeVaul, Laura Smith, Margaret Checchi, Daniel Martin, Leslie A Hoffman, Kirstin M Brown, Daniel J Mumbower, Robert M Becker, Victoria A Roach, Alison F Doubleday, Danielle N Edwards, Rebecca S Lufler, Alexandra Wactor, Sophia Boxerman, Suzanne Smith, Hannah Herriott, Adam B Wilson","doi":"10.1080/0142159X.2025.2564869","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Medical schools would benefit from systematic guidance for developing comprehensive artificial intelligence (AI) policies, given generative AI's rapid integration into medical education. This study developed and applied an idealized AI policy framework to analyze AI-related documents at U.S. medical school institutions, providing reference points for the development and refinement of institutional policies.</p><p><strong>Methods: </strong>AI-related documents from institutions with U.S. allopathic and osteopathic medical schools were systematically collected (from August to October 2024) and analyzed using a comprehensive framework containing 24 subthemes across six themes: Background/Context, Governance, AI Literacy, Tools/Usage, Ethical/Legal Considerations, and Technology Support and Infrastructure. Publicly available online documents were systematically coded to generate framework subtheme scores indicating breadth of coverage across framework themes.</p><p><strong>Results: </strong>AI-related documents retrieved from 73.7% (146/198) of U.S. medical school institutions covered an average of 8 of 24 subthemes, representing a mean framework coverage score of 32.3% ± 19.8 Rarely addressed subthemes included Audit and Compliance Mechanisms (6.8%, 10/146), Technical Infrastructure (6.2%, 9/146), and Environmental Stewardship (1.4%, 2/146). Academic Honesty and Plagiarism dominated AI-related documents (81.5%, 119/146), followed by Decision-Making Authority (54.1%, 79/146) and Critical Evaluation (52.1%, 76/146). Formal AI policies demonstrated significantly higher framework coverage than other AI document types (44.0% vs 30.4%, <i>p</i> = 0.003). Seven institutions with the highest coverage (≥13/24 subthemes) shared seven common distinguishing features, with six present universally.</p><p><strong>Conclusions: </strong>AI-related documents currently emphasize academic integrity over strategic planning, with substantial gaps in infrastructure and review mechanisms. Institutions can enhance their AI policies by incorporating common features identified in well-designed policies and following frameworks that strike a balance between immediate concerns and long-term adaptability.</p>","PeriodicalId":18643,"journal":{"name":"Medical Teacher","volume":" ","pages":"1-13"},"PeriodicalIF":3.3000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An audit of AI-related documents across U.S. medical schools: A framework-based qualitative content analysis.\",\"authors\":\"Emily Rush, Jessica N Byram, Colleen N Garnett, Nicole DeVaul, Laura Smith, Margaret Checchi, Daniel Martin, Leslie A Hoffman, Kirstin M Brown, Daniel J Mumbower, Robert M Becker, Victoria A Roach, Alison F Doubleday, Danielle N Edwards, Rebecca S Lufler, Alexandra Wactor, Sophia Boxerman, Suzanne Smith, Hannah Herriott, Adam B Wilson\",\"doi\":\"10.1080/0142159X.2025.2564869\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Medical schools would benefit from systematic guidance for developing comprehensive artificial intelligence (AI) policies, given generative AI's rapid integration into medical education. 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Publicly available online documents were systematically coded to generate framework subtheme scores indicating breadth of coverage across framework themes.</p><p><strong>Results: </strong>AI-related documents retrieved from 73.7% (146/198) of U.S. medical school institutions covered an average of 8 of 24 subthemes, representing a mean framework coverage score of 32.3% ± 19.8 Rarely addressed subthemes included Audit and Compliance Mechanisms (6.8%, 10/146), Technical Infrastructure (6.2%, 9/146), and Environmental Stewardship (1.4%, 2/146). Academic Honesty and Plagiarism dominated AI-related documents (81.5%, 119/146), followed by Decision-Making Authority (54.1%, 79/146) and Critical Evaluation (52.1%, 76/146). Formal AI policies demonstrated significantly higher framework coverage than other AI document types (44.0% vs 30.4%, <i>p</i> = 0.003). Seven institutions with the highest coverage (≥13/24 subthemes) shared seven common distinguishing features, with six present universally.</p><p><strong>Conclusions: </strong>AI-related documents currently emphasize academic integrity over strategic planning, with substantial gaps in infrastructure and review mechanisms. Institutions can enhance their AI policies by incorporating common features identified in well-designed policies and following frameworks that strike a balance between immediate concerns and long-term adaptability.</p>\",\"PeriodicalId\":18643,\"journal\":{\"name\":\"Medical Teacher\",\"volume\":\" \",\"pages\":\"1-13\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical Teacher\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1080/0142159X.2025.2564869\",\"RegionNum\":2,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION, SCIENTIFIC DISCIPLINES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Teacher","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1080/0142159X.2025.2564869","RegionNum":2,"RegionCategory":"教育学","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
目的：鉴于生成式人工智能迅速融入医学教育，医学院将受益于制定综合人工智能（AI）政策的系统指导。本研究开发并应用了一个理想化的人工智能政策框架来分析美国医学院机构的人工智能相关文件，为机构政策的制定和完善提供参考点。方法：系统收集美国对抗疗法和整骨疗法医学院机构的人工智能相关文件（2024年8月至10月），并使用包含6个主题的24个子主题的综合框架进行分析：背景/背景，治理，人工智能素养，工具/使用，道德/法律考虑以及技术支持和基础设施。公开可用的在线文件被系统地编码，以生成框架子主题得分，表明跨框架主题的覆盖广度。结果：从73.7%（146/198）的美国医学院机构检索到的人工智能相关文件平均覆盖了24个子主题中的8个，平均框架覆盖率为32.3%±19.8。很少涉及的子主题包括审计和合规机制（6.8%,10/146）、技术基础设施（6.2%,9/146）和环境管理（1.4%,2/146）。人工智能相关文献以学术诚信和剽窃为主（81.5%,119/146），其次是决策权威（54.1%,79/146）和批判性评价（52.1%,76/146）。正式的人工智能政策比其他人工智能文件类型显示出更高的框架覆盖率（44.0% vs 30.4%, p = 0.003）。覆盖率最高的7个机构（≥13/24个子主题）有7个共同的显著特征，其中6个普遍存在。结论：目前人工智能相关文件强调学术诚信甚于战略规划，基础设施和审查机制存在较大差距。机构可以通过纳入精心设计的政策中确定的共同特征，并遵循在当前关注和长期适应性之间取得平衡的框架，来增强其人工智能政策。
An audit of AI-related documents across U.S. medical schools: A framework-based qualitative content analysis.
Purpose: Medical schools would benefit from systematic guidance for developing comprehensive artificial intelligence (AI) policies, given generative AI's rapid integration into medical education. This study developed and applied an idealized AI policy framework to analyze AI-related documents at U.S. medical school institutions, providing reference points for the development and refinement of institutional policies.
Methods: AI-related documents from institutions with U.S. allopathic and osteopathic medical schools were systematically collected (from August to October 2024) and analyzed using a comprehensive framework containing 24 subthemes across six themes: Background/Context, Governance, AI Literacy, Tools/Usage, Ethical/Legal Considerations, and Technology Support and Infrastructure. Publicly available online documents were systematically coded to generate framework subtheme scores indicating breadth of coverage across framework themes.
Results: AI-related documents retrieved from 73.7% (146/198) of U.S. medical school institutions covered an average of 8 of 24 subthemes, representing a mean framework coverage score of 32.3% ± 19.8 Rarely addressed subthemes included Audit and Compliance Mechanisms (6.8%, 10/146), Technical Infrastructure (6.2%, 9/146), and Environmental Stewardship (1.4%, 2/146). Academic Honesty and Plagiarism dominated AI-related documents (81.5%, 119/146), followed by Decision-Making Authority (54.1%, 79/146) and Critical Evaluation (52.1%, 76/146). Formal AI policies demonstrated significantly higher framework coverage than other AI document types (44.0% vs 30.4%, p = 0.003). Seven institutions with the highest coverage (≥13/24 subthemes) shared seven common distinguishing features, with six present universally.
Conclusions: AI-related documents currently emphasize academic integrity over strategic planning, with substantial gaps in infrastructure and review mechanisms. Institutions can enhance their AI policies by incorporating common features identified in well-designed policies and following frameworks that strike a balance between immediate concerns and long-term adaptability.
期刊介绍：
Medical Teacher provides accounts of new teaching methods, guidance on structuring courses and assessing achievement, and serves as a forum for communication between medical teachers and those involved in general education. In particular, the journal recognizes the problems teachers have in keeping up-to-date with the developments in educational methods that lead to more effective teaching and learning at a time when the content of the curriculum—from medical procedures to policy changes in health care provision—is also changing. The journal features reports of innovation and research in medical education, case studies, survey articles, practical guidelines, reviews of current literature and book reviews. All articles are peer reviewed.
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