Karim Hanna, David Chartash, Winston Liaw, Damian Archer, Daniel Parente, Nipa R Shah, Steven Waldren, Bernard Ewigman, Wayne Altman
{"title":"Family Medicine Must Prepare for Artificial Intelligence.","authors":"Karim Hanna, David Chartash, Winston Liaw, Damian Archer, Daniel Parente, Nipa R Shah, Steven Waldren, Bernard Ewigman, Wayne Altman","doi":"10.3122/jabfm.2023.230360R1","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial Intelligence (AI) is poised to revolutionize family medicine, offering a transformative approach to achieving the Quintuple Aim. This article examines the imperative for family medicine to adapt to the rapidly evolving field of AI, with an emphasis on its integration in clinical practice. AI's recent advancements have the potential to significantly transform health care. We argue for the proactive engagement of family medicine in directing AI technologies toward enhancing the \"Quintuple Aim.\"The article highlights potential benefits of AI, such as improved patient outcomes through enhanced diagnostic tools, clinician well-being through reduced administrative burdens, and the promotion of health equity by analyzing diverse data sets. However, we also acknowledge the risks associated with AI, including the potential for automation to diverge from patient-centered care and exacerbate health care disparities. Our recommendations stress the need for family medicine education to incorporate AI literacy, the development of a collaborative for AI integration, and the establishment of guidelines and standards through interdisciplinary cooperation. We conclude that although AI poses challenges, its responsible and ethical implementation can revolutionize family medicine, optimizing patient care and enhancing the role of clinicians in a technology-driven future.</p>","PeriodicalId":50018,"journal":{"name":"Journal of the American Board of Family Medicine","volume":"37 4","pages":"520-524"},"PeriodicalIF":2.4000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Board of Family Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3122/jabfm.2023.230360R1","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial Intelligence (AI) is poised to revolutionize family medicine, offering a transformative approach to achieving the Quintuple Aim. This article examines the imperative for family medicine to adapt to the rapidly evolving field of AI, with an emphasis on its integration in clinical practice. AI's recent advancements have the potential to significantly transform health care. We argue for the proactive engagement of family medicine in directing AI technologies toward enhancing the "Quintuple Aim."The article highlights potential benefits of AI, such as improved patient outcomes through enhanced diagnostic tools, clinician well-being through reduced administrative burdens, and the promotion of health equity by analyzing diverse data sets. However, we also acknowledge the risks associated with AI, including the potential for automation to diverge from patient-centered care and exacerbate health care disparities. Our recommendations stress the need for family medicine education to incorporate AI literacy, the development of a collaborative for AI integration, and the establishment of guidelines and standards through interdisciplinary cooperation. We conclude that although AI poses challenges, its responsible and ethical implementation can revolutionize family medicine, optimizing patient care and enhancing the role of clinicians in a technology-driven future.
期刊介绍:
Published since 1988, the Journal of the American Board of Family Medicine ( JABFM ) is the official peer-reviewed journal of the American Board of Family Medicine (ABFM). Believing that the public and scientific communities are best served by open access to information, JABFM makes its articles available free of charge and without registration at www.jabfm.org. JABFM is indexed by Medline, Index Medicus, and other services.