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人工智能在医学教育和康复医学中的指导作用

Artificial Intelligence in Medical Education and Mentoring in Rehabilitation Medicine.

机构信息

From the Department of Orthopedics, Wake Forest University School of Medicine, Winston-Salem, North Carolina (JKS); Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts (NG); Spaulding Rehabilitation Hospital, Charlestown, Massachusetts (MRD, NG); and MGH Institute of Health Professions, Boston, Massachusetts (NG).

出版信息

Am J Phys Med Rehabil. 2024 Nov 1;103(11):1039-1044. doi: 10.1097/PHM.0000000000002604. Epub 2024 Jul 15.

DOI:10.1097/PHM.0000000000002604
PMID:39016292
Abstract

Artificial intelligence emerges as a transformative force, offering novel solutions to enhance medical education and mentorship in the specialty of physical medicine and rehabilitation. Artificial intelligence is a transformative technology that is being adopted in nearly every industry. In medicine, the use of artificial intelligence in medical education is growing. Artificial intelligence may also assist with some of the challenges of mentorship, including the limited availability of experienced mentors, and the logistical difficulties of time and geography are some constraints of traditional mentorship. In this commentary, we discuss various models of artificial intelligence in medical education and mentoring, including expert systems, conversational agents, and hybrid models. These models enable tailored guidance, broaden outreach within the physical medicine and rehabilitation community, and support continuous learning and development. Balancing artificial intelligence's technical advantages with the essential human elements while addressing ethical considerations, artificial intelligence integration into medical education and mentorship presents a paradigm shift toward a more accessible, responsive, and enriched experience in rehabilitation medicine.

摘要

人工智能作为一种变革性的力量出现,为物理医学与康复专业的医学教育和指导提供了新颖的解决方案。人工智能是一种变革性的技术,几乎在每个行业都得到了应用。在医学领域,人工智能在医学教育中的应用正在不断增加。人工智能也可能有助于解决一些指导方面的挑战,包括经验丰富的导师的有限可用性,以及传统指导的时间和地理方面的困难等限制。在这篇评论中,我们讨论了医学教育和指导中的各种人工智能模式,包括专家系统、对话代理和混合模型。这些模型可以实现个性化的指导,扩大物理医学与康复领域的影响力,并支持持续的学习和发展。在考虑伦理问题的同时,平衡人工智能的技术优势和必要的人为因素,将人工智能融入医学教育和指导中,代表了康复医学领域朝着更便捷、更有响应性和更丰富的体验的范式转变。

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