Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools
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| Format: | Preprint |
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2024
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| author | Ma, Yingbo Song, Yukyeong Balch, Jeremy A. Ren, Yuanfang Vellanki, Divya Hu, Zhenhong Brennan, Meghan Kolla, Suraj Guan, Ziyuan Armfield, Brooke Ozrazgat-Baslanti, Tezcan Rashidi, Parisa Loftus, Tyler J. Bihorac, Azra Shickel, Benjamin |
| author_facet | Ma, Yingbo Song, Yukyeong Balch, Jeremy A. Ren, Yuanfang Vellanki, Divya Hu, Zhenhong Brennan, Meghan Kolla, Suraj Guan, Ziyuan Armfield, Brooke Ozrazgat-Baslanti, Tezcan Rashidi, Parisa Loftus, Tyler J. Bihorac, Azra Shickel, Benjamin |
| contents | As more clinical workflows continue to be augmented by artificial intelligence (AI), AI literacy among physicians will become a critical requirement for ensuring safe and ethical AI-enabled patient care. Despite the evolving importance of AI in healthcare, the extent to which it has been adopted into traditional and often-overloaded medical curricula is currently unknown. In a scoping review of 1,699 articles published between January 2016 and June 2024, we identified 18 studies which propose guiding frameworks, and 11 studies documenting real-world instruction, centered around the integration of AI into medical education. We found that comprehensive guidelines will require greater clinical relevance and personalization to suit medical student interests and career trajectories. Current efforts highlight discrepancies in the teaching guidelines, emphasizing AI evaluation and ethics over technical topics such as data science and coding. Additionally, we identified several challenges associated with integrating AI training into the medical education program, including a lack of guidelines to define medical students AI literacy, a perceived lack of proven clinical value, and a scarcity of qualified instructors. With this knowledge, we propose an AI literacy framework to define competencies for medical students. To prioritize relevant and personalized AI education, we categorize literacy into four dimensions: Foundational, Practical, Experimental, and Ethical, with tailored learning objectives to the pre-clinical, clinical, and clinical research stages of medical education. This review provides a road map for developing practical and relevant education strategies for building an AI-competent healthcare workforce. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18939 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools Ma, Yingbo Song, Yukyeong Balch, Jeremy A. Ren, Yuanfang Vellanki, Divya Hu, Zhenhong Brennan, Meghan Kolla, Suraj Guan, Ziyuan Armfield, Brooke Ozrazgat-Baslanti, Tezcan Rashidi, Parisa Loftus, Tyler J. Bihorac, Azra Shickel, Benjamin Computers and Society Artificial Intelligence As more clinical workflows continue to be augmented by artificial intelligence (AI), AI literacy among physicians will become a critical requirement for ensuring safe and ethical AI-enabled patient care. Despite the evolving importance of AI in healthcare, the extent to which it has been adopted into traditional and often-overloaded medical curricula is currently unknown. In a scoping review of 1,699 articles published between January 2016 and June 2024, we identified 18 studies which propose guiding frameworks, and 11 studies documenting real-world instruction, centered around the integration of AI into medical education. We found that comprehensive guidelines will require greater clinical relevance and personalization to suit medical student interests and career trajectories. Current efforts highlight discrepancies in the teaching guidelines, emphasizing AI evaluation and ethics over technical topics such as data science and coding. Additionally, we identified several challenges associated with integrating AI training into the medical education program, including a lack of guidelines to define medical students AI literacy, a perceived lack of proven clinical value, and a scarcity of qualified instructors. With this knowledge, we propose an AI literacy framework to define competencies for medical students. To prioritize relevant and personalized AI education, we categorize literacy into four dimensions: Foundational, Practical, Experimental, and Ethical, with tailored learning objectives to the pre-clinical, clinical, and clinical research stages of medical education. This review provides a road map for developing practical and relevant education strategies for building an AI-competent healthcare workforce. |
| title | Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2407.18939 |