{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:41:43Z","timestamp":1784198503930,"version":"3.55.0"},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"5-6","funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["abc-001"],"award-info":[{"award-number":["abc-001"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["abc-002"],"award-info":[{"award-number":["abc-002"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,18]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>The success of deep-learning algorithms in analyzing complex structured and unstructured multidimensional data has caused an exponential increase in the amount of research devoted to the applications of artificial intelligence (AI) in medicine in the past decade. Public release of large language models like ChatGPT the past year has generated an unprecedented storm of excitement and rumors of machine intelligence finally reaching or even surpassing human capability in detecting meaningful signals in complex multivariate data. Such enthusiasm, however, is met with an equal degree of both skepticism and fear over the social, legal, and moral implications of such powerful technology with relatively little safeguards or regulations on its development. The question remains in medicine of how to harness the power of AI to improve patient outcomes by increasing the diagnostic accuracy and treatment precision provided by medical professionals. Military medicine, given its unique mission and resource constraints,can benefit immensely from such technology. However, reaping such benefits hinges on the ability of the rising generations of military medical professionals to understand AI algorithms and their applications. Additionally, they should strongly consider working with them as an adjunct decision-maker and view them as a colleague to access and harness relevant information as opposed to something to be feared. Ideas expressed in this commentary were formulated by a military medical student during a two-month research elective working on a multidisciplinary team of computer scientists and clinicians at the National Library of Medicine advancing the state of the art of AI in medicine. A motivation to incorporate AI in the Military Health System is provided, including examples of applications in military medicine. Rationale is then given for inclusion of AI in education starting in medical school as well as a prudent implementation of these algorithms in a clinical workflow during graduate medical education. Finally, barriers to implementation are addressed along with potential solutions. The end state is not that rising military physicians are technical experts in AI; but rather that they understand how they can leverage its rapidly evolving capabilities to prepare for a future where AI will have a significant role in clinical care. The overall goal is to develop trained clinicians that can leverage these technologies to improve the Military Health System.<\/jats:p>","DOI":"10.1093\/milmed\/usad412","type":"journal-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T18:26:00Z","timestamp":1697912760000},"page":"954-958","source":"Crossref","is-referenced-by-count":11,"title":["The Need for\u00a0Artificial Intelligence Curriculum in\u00a0Military Medical Education"],"prefix":"10.1093","volume":"189","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2579-669X","authenticated-orcid":false,"given":"Jonathan R","family":"Spirnak","sequence":"first","affiliation":[{"name":"Uniformed Services University of the Health Sciences (USUHS) School of Medicine , Bethesda, MD 20814,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0040-1387","authenticated-orcid":false,"given":"Sameer","family":"Antani","sequence":"additional","affiliation":[{"name":"Computational Health Research Branch, National Library of Medicine, National Institutes of Health , Bethesda, MD 20894,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,10,20]]},"reference":[{"issue":"7873","key":"2025012315141793500_R1","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"issue":"2","key":"2025012315141793500_R2","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1371\/journal.pdig.0000198","article-title":"Performance of\u00a0ChatGPT on\u00a0USMLE: potential for\u00a0AI-assisted medical education using large language models","volume":"2","author":"Kung","year":"2023","journal-title":"PLOS Digit Health"},{"issue":"6","key":"2025012315141793500_R3","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1001\/jamainternmed.2023.1838","article-title":"Comparing physician and\u00a0artificial intelligence chatbot responses to patient questions posted to a public social media forum","volume":"183","author":"Ayers","year":"2023","journal-title":"JAMA Intern Med"},{"key":"2025012315141793500_R4","article-title":"Oxford reference","author":"Artificial Intelligence","year":"2023"},{"key":"2025012315141793500_R5","first-page":"e101","volume-title":"Learning Radiology","author":"Herring","year":"2024","edition":"5th"},{"issue":"4","key":"2025012315141793500_R6","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1056\/NEJMra2204673","article-title":"Artificial intelligence in\u00a0U.S. Health Care Delivery","volume":"389","author":"Sahni","year":"2023","journal-title":"N Engl J Med"},{"issue":"3","key":"2025012315141793500_R7","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.jacc.2020.11.030","article-title":"Machine learning and\u00a0the future of\u00a0cardiovascular care","volume":"77","author":"Quer","year":"2021","journal-title":"J Am Coll Cardiol"},{"issue":"5","key":"2025012315141793500_R8","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1056\/NEJMp2304993","article-title":"AI and\u00a0medical education - 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