Gary Takahashi, Laurentius von Liechti, Ebrahim Tarshizi
{"title":"Quo vadis, \"AI-empowered Doctor\"?","authors":"Gary Takahashi, Laurentius von Liechti, Ebrahim Tarshizi","doi":"10.2196/70079","DOIUrl":null,"url":null,"abstract":"<p><strong>Unstructured: </strong>In the first decade of this century, physicians maintained considerable professional autonomy, enabling discretionary evaluation and implementation of these technologies according to individual practice requirements. The past decade, however, has witnessed significant restructuring of medical practice patterns, with most physicians transitioning to employed status. Concurrently, technological advances and other incentives drove the implementation of electronic systems into the clinic, which these physicians were compelled to integrate. Healthcare practitioners have now been introduced to applications based on Large Language Models, largely driven by AI developers as well as established EHR vendors eager to incorporate these innovations. While Generative AI assistance promises enhanced clinical efficiency and diagnostic precision, its rapid advancement may potentially redefine clinical provider roles and transform workflows, as it has already altered expectations of physician productivity, as well as introduced unprecedented liability considerations. Recognition of the input of physicians and other clinical stakeholders in this nascent stage of AI integration is essential. This requires a more comprehensive understanding of AI as a sophisticated clinical tool. Accordingly, we advocate for its systematic incorporation into standard medical curriculum.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/70079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0
Abstract
Unstructured: In the first decade of this century, physicians maintained considerable professional autonomy, enabling discretionary evaluation and implementation of these technologies according to individual practice requirements. The past decade, however, has witnessed significant restructuring of medical practice patterns, with most physicians transitioning to employed status. Concurrently, technological advances and other incentives drove the implementation of electronic systems into the clinic, which these physicians were compelled to integrate. Healthcare practitioners have now been introduced to applications based on Large Language Models, largely driven by AI developers as well as established EHR vendors eager to incorporate these innovations. While Generative AI assistance promises enhanced clinical efficiency and diagnostic precision, its rapid advancement may potentially redefine clinical provider roles and transform workflows, as it has already altered expectations of physician productivity, as well as introduced unprecedented liability considerations. Recognition of the input of physicians and other clinical stakeholders in this nascent stage of AI integration is essential. This requires a more comprehensive understanding of AI as a sophisticated clinical tool. Accordingly, we advocate for its systematic incorporation into standard medical curriculum.