Preethi Jagannath, Nichelle Perera, Laura Minton, Rachel Bass, Desmin Milner, Jordan Perchik
{"title":"增加可及性:国际医学毕业生远程人工智能教育课程的有效性","authors":"Preethi Jagannath, Nichelle Perera, Laura Minton, Rachel Bass, Desmin Milner, Jordan Perchik","doi":"10.1111/tct.70047","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Applications of artificial intelligence (AI) in medicine are expanding every year. AI education is crucial to its appropriate use in healthcare; however, most US medical schools lack a dedicated AI curriculum. These resources are sparse for international medical graduates (IMGs). Using the Artificial Intelligence in Radiology Education (AIRE) curriculum, we assessed the radiology AI course's effectiveness in increasing the AI competency of IMGs.</p>\n </section>\n \n <section>\n \n <h3> Approach</h3>\n \n <p>AIRE curriculum features nine free YouTube lectures on AI in radiology. Participants watched lectures remotely on fundamental AI terms, methods, clinical applications and special topics. They completed a pre- and post-course e-survey and assessment. The survey assessed participants' prior AI experience, subjective knowledge and opinions on the need for AI in medical education. The assessment determined participants' knowledge of AI. Pre- and post-course assessment scores were compared using a Student's <i>t</i>-test to determine if the course increased participant knowledge of AI terms and applications.</p>\n </section>\n \n <section>\n \n <h3> Evaluation</h3>\n \n <p>Three hundred fifty-seven students from 28 countries enrolled in the course; 175 completed the course within the study period. Nearly all participants reported insufficient AI exposure in their radiology training (91.3%). Participants' knowledge of fundamental AI terms and methods increased after completion of the course, with an average pre-course assessment score of 6.5/15 and a post-course assessment score of 9.4/15 (<i>p</i> < 0.0001).</p>\n </section>\n \n <section>\n \n <h3> Implications</h3>\n \n <p>AIRE curriculum's effectiveness demonstrates that a remote education course is a viable model to bring accessible fundamental AI education to international medical students. Remote education curricula in medical AI can help mitigate disparities in AI education for lower resource medical programmes.</p>\n </section>\n </div>","PeriodicalId":47324,"journal":{"name":"Clinical Teacher","volume":"22 2","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Increasing Accessibility: Effectiveness of a Remote Artificial Intelligence Education Curriculum for International Medical Graduates\",\"authors\":\"Preethi Jagannath, Nichelle Perera, Laura Minton, Rachel Bass, Desmin Milner, Jordan Perchik\",\"doi\":\"10.1111/tct.70047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Applications of artificial intelligence (AI) in medicine are expanding every year. AI education is crucial to its appropriate use in healthcare; however, most US medical schools lack a dedicated AI curriculum. These resources are sparse for international medical graduates (IMGs). Using the Artificial Intelligence in Radiology Education (AIRE) curriculum, we assessed the radiology AI course's effectiveness in increasing the AI competency of IMGs.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Approach</h3>\\n \\n <p>AIRE curriculum features nine free YouTube lectures on AI in radiology. Participants watched lectures remotely on fundamental AI terms, methods, clinical applications and special topics. They completed a pre- and post-course e-survey and assessment. The survey assessed participants' prior AI experience, subjective knowledge and opinions on the need for AI in medical education. The assessment determined participants' knowledge of AI. Pre- and post-course assessment scores were compared using a Student's <i>t</i>-test to determine if the course increased participant knowledge of AI terms and applications.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Evaluation</h3>\\n \\n <p>Three hundred fifty-seven students from 28 countries enrolled in the course; 175 completed the course within the study period. Nearly all participants reported insufficient AI exposure in their radiology training (91.3%). Participants' knowledge of fundamental AI terms and methods increased after completion of the course, with an average pre-course assessment score of 6.5/15 and a post-course assessment score of 9.4/15 (<i>p</i> < 0.0001).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Implications</h3>\\n \\n <p>AIRE curriculum's effectiveness demonstrates that a remote education course is a viable model to bring accessible fundamental AI education to international medical students. 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Increasing Accessibility: Effectiveness of a Remote Artificial Intelligence Education Curriculum for International Medical Graduates
Background
Applications of artificial intelligence (AI) in medicine are expanding every year. AI education is crucial to its appropriate use in healthcare; however, most US medical schools lack a dedicated AI curriculum. These resources are sparse for international medical graduates (IMGs). Using the Artificial Intelligence in Radiology Education (AIRE) curriculum, we assessed the radiology AI course's effectiveness in increasing the AI competency of IMGs.
Approach
AIRE curriculum features nine free YouTube lectures on AI in radiology. Participants watched lectures remotely on fundamental AI terms, methods, clinical applications and special topics. They completed a pre- and post-course e-survey and assessment. The survey assessed participants' prior AI experience, subjective knowledge and opinions on the need for AI in medical education. The assessment determined participants' knowledge of AI. Pre- and post-course assessment scores were compared using a Student's t-test to determine if the course increased participant knowledge of AI terms and applications.
Evaluation
Three hundred fifty-seven students from 28 countries enrolled in the course; 175 completed the course within the study period. Nearly all participants reported insufficient AI exposure in their radiology training (91.3%). Participants' knowledge of fundamental AI terms and methods increased after completion of the course, with an average pre-course assessment score of 6.5/15 and a post-course assessment score of 9.4/15 (p < 0.0001).
Implications
AIRE curriculum's effectiveness demonstrates that a remote education course is a viable model to bring accessible fundamental AI education to international medical students. Remote education curricula in medical AI can help mitigate disparities in AI education for lower resource medical programmes.
期刊介绍:
The Clinical Teacher has been designed with the active, practising clinician in mind. It aims to provide a digest of current research, practice and thinking in medical education presented in a readable, stimulating and practical style. The journal includes sections for reviews of the literature relating to clinical teaching bringing authoritative views on the latest thinking about modern teaching. There are also sections on specific teaching approaches, a digest of the latest research published in Medical Education and other teaching journals, reports of initiatives and advances in thinking and practical teaching from around the world, and expert community and discussion on challenging and controversial issues in today"s clinical education.