{"id":5822,"date":"2024-04-22T10:52:50","date_gmt":"2024-04-22T15:52:50","guid":{"rendered":"https:\/\/lsom.uthscsa.edu\/family-medicine\/?page_id=5822"},"modified":"2024-04-22T11:22:48","modified_gmt":"2024-04-22T16:22:48","slug":"advancing-artificial-intelligence-and-machine-learning","status":"publish","type":"page","link":"https:\/\/lsom.uthscsa.edu\/family-medicine\/advancing-artificial-intelligence-and-machine-learning\/","title":{"rendered":"Primary Care Week: Advancing Artificial Intelligence and Machine Learning"},"content":{"rendered":"

[vc_row][vc_column width=”2\/3″][vc_column_text css=””]<\/p>\n

Overview:<\/h3>\n

Advancing Artificial Intelligence and Machine Learning<\/strong>
\nfor the Future of Primary Care and Population Health<\/strong>
\nSteven Lin, MD<\/strong><\/p>\n

Despite being the largest U.S. health care delivery platform, primary care is at risk of being left behind in the research, development, and implementation of artificial intelligence and machine learning (AI\/ML) applications. Although primary care accounts for 52% of all care delivered \u2013 more than all other specialties combined \u2013 only 3% of FDA approved AI\/ML tools is intended for primary care, and less than 1% of AI\/ML research funding goes to primary care. To ensure that the power, opportunity, and future of health care AI\/ML are realized in the broadest scale, AI\/ML developers must pay more attention to primary care, and primary care leaders must do more to engage in AI\/ML research and implementation. In this session, Dr. Steven Lin from Stanford University School of Medicine will discuss how to advance AI\/ML for the future of primary care and population health through effective cross-sectoral partnerships, investment in implementation science, and a focus on health policy and equity. Participants will explore promising AI\/ML use cases and learn how primary care organizations can collaborate to realize a shared vision of AI\/ML technologies built by primary care, for primary care.<\/p>\n

By the end of this presentation, attendees will be able to:<\/p>\n

1) Describe 3 major gaps in health care AI\/ML research,
\ndevelopment, and implementation<\/p>\n

2) Explain how primary care can address these gaps<\/p>\n

3) List at least 6 use cases for AI\/ML in primary care and population
\nhealth<\/p>\n

Presenter:<\/h3>\n

\"\"<\/strong><\/p>\n

Steven Lin, MD<\/strong><\/p>\n

Bellue-Holly Distinguished Visiting Professor
\nSection Chief, General Primary Care &
\nVice Chief of Technology Innovation, Division of
\nPrimary Care & Population Health,
\nStanford University<\/p>\n

Financial Disclosures:<\/h3>\n

Stephen Lin, MD has disclosed that he is a paid consultant for Codex Health and a principal investigator for the following organizations: Amazon, American Academy of Family Physicians, American Board of Family Medicine, Center for Professionalism and Value in Health Care, Codex Health, DeepScribe, Google Health, Omada Health, Predicta Med, Quadrant Technologies, Soap Health, Society of Teachers of Family Medicine, University of California San Francisco, and Verily. The relevant financial relationships noted for Dr. Lin have been mitigated.<\/p>\n

The Family & Community Medicine Professional Development and Grand Rounds Committee members (Mark Nadeau, MD, MBA; Nehman Andry, MD; Maria Del Pilar Montanez Villacampa, MD, Stacy Ogbeide, PsyD; Yun Shi, MD, PhD; Marcy Wiemers, MD; Christine Song, DO; Gabriela Gibson-Lopez, PsyD; Margaret Finley, MD; Richel Avery, MD; Khorshid Amirkhosravi, MD; Jeff Svec, MD; Oralia Bazaldua, Pharm D; Kelsi Sexton Barajas, MA, MEd) have no financial relationships with ineligible companies to disclose.<\/p>\n

The Family & Community Medicine Professional Development and Grand Rounds Committee member Carlos Roberto Ja\u00e9n, MD has disclosed he receives royalties from General Practice and Family Medicine for being UpToDate Editor-in-Chief.<\/p>\n

Credits:<\/h3>\n

AMA PRA Category 1 Credits\u2122<\/em> (1.00 hours), Non-Physician Participation Credit (1.00 hours)<\/p>\n

Target Audience:<\/h3>\n

Specialties \u2013 Primary Care; Family Medicine
\nFaculty, residents, other health care providers and staff from our department; physicians and health care providers from San Antonio and South Texas; and medical students in our third-year clerkship and fourth year rotations.<\/p>\n

Accreditation:<\/h3>\n

The UT Health Science Center San Antonio is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.<\/p>\n

Credit Designation:<\/h3>\n

The UT Health Science Center San Antonio designates this live activity up to a maximum of\u00a01.00 AMA PRA Category 1 Credits\u2122<\/em>.<\/p>\n

Physicians should claim only the credit commensurate with the extent of their participation in the activity.<\/p>\n

Nurses and other healthcare professionals will receive a Certificate of Attendance. For information on applicability and acceptance, please consult your professional licensing board.[\/vc_column_text][\/vc_column][vc_column width=”1\/3″] \n \n <\/i><\/span><\/span>
\n

View Recording (CME Credit)<\/h3>\n

<\/p>\n <\/a> \n \n <\/i><\/span><\/span>
\n

View Recording (no CME Credit)<\/h3>\n

<\/p>\n <\/a>[vc_column_text]For activity related questions, please contact:
\nName: Kelsi Sexton Barajas- FCM Grand Rounds Coordinator
\nEmail: sextonk@uthscsa.edu<\/a><\/p>\n

For CME general questions, please contact
\nPh: (210) 567-4445
\nEmail: cme@uthscsa.edu<\/a>[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

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Although […]<\/p>\n","protected":false},"author":424,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-5822","page","type-page","status-publish","hentry"],"yoast_head":"\nPrimary Care Week: Advancing Artificial Intelligence and Machine Learning - Family and Community Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/lsom.uthscsa.edu\/family-medicine\/advancing-artificial-intelligence-and-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Primary Care Week: Advancing Artificial Intelligence and Machine Learning - Family and Community Medicine\" \/>\n<meta property=\"og:description\" content=\"[vc_row][vc_column width=”2\/3″][vc_column_text css=””] Overview: Advancing Artificial Intelligence and Machine Learning for the Future of Primary Care and Population Health Steven Lin, MD Despite being the largest U.S. health care delivery platform, primary care is at risk of being left behind in the research, development, and implementation of artificial intelligence and machine learning (AI\/ML) applications. 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