Medical Students’ Attitudes Toward AI in Medicine and their Expectations for Medical Education

医学教育 背景(考古学) 医学诊断 多样性(控制论) 可信赖性 心理学 医学伦理学 人工智能 计算机科学 医学 古生物学 社会心理学 病理 精神科 生物
作者
Joachim Kimmerle,Jasmin Timm,Teresa Festl‐Wietek,Ulrike Creß,Anne Herrmann–Werner
出处
期刊:Journal of medical education and curricular development [SAGE]
卷期号:10 被引量:8
标识
DOI:10.1177/23821205231219346
摘要

Objectives Artificial intelligence (AI) is used in a variety of contexts in medicine. This involves the use of algorithms and software that analyze digital information to make diagnoses and suggest adapted therapies. It is unclear, however, what medical students know about AI in medicine, how they evaluate its application, and what they expect from their medical training accordingly. In the study presented here, we aimed at providing answers to these questions. Methods In this survey study, we asked medical students about their assessment of AI in medicine and recorded their ideas and suggestions for considering this topic in medical education. Fifty-eight medical students completed the survey. Results Almost all participants were aware of the use of AI in medicine and had an adequate understanding of it. They perceived AI in medicine to be reliable, trustworthy, and technically competent, but did not have much faith in it. They considered AI in medicine to be rather intelligent but not anthropomorphic. Participants were interested in the opportunities of AI in the medical context and wanted to learn more about it. They indicated that basic AI knowledge should be taught in medical studies, in particular, knowledge about modes of operation, ethics, areas of application, reliability, and possible risks. Conclusions We discuss the implications of these findings for the curricular development in medical education. Medical students need to be equipped with the knowledge and skills to use AI effectively and ethically in their future practice. This includes understanding the limitations and potential biases of AI algorithms by teaching the sensible use of human oversight and continuous monitoring to catch errors in AI algorithms and ensure that final decisions are made by human clinicians.

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