Ensuring useful adoption of generative artificial intelligence in healthcare

Author:

Jindal Jenelle A1ORCID,Lungren Matthew P234ORCID,Shah Nigam H567ORCID

Affiliation:

1. Center for Biomedical Informatics Research, Stanford University , Stanford, CA 94305, United States

2. Health and Life Sciences, Microsoft Corporation , Redmond, WA 98052, United States

3. Department of Biomedical Data Science, Stanford University School of Medicine , Stanford, CA 94305, United States

4. Department of Biomedical Imaging, University of California San Francisco , San Francisco, CA 94143, United States

5. Department of Medicine, Stanford School of Medicine , Stanford, CA 94304, United States

6. Clinical Excellence Research Center, Stanford School of Medicine , Stanford, CA 94304, United States

7. Technology and Digital Solutions, Stanford Health Care , Palo Alto, CA 94304, United States

Abstract

Abstract Objectives This article aims to examine how generative artificial intelligence (AI) can be adopted with the most value in health systems, in response to the Executive Order on AI. Materials and Methods We reviewed how technology has historically been deployed in healthcare, and evaluated recent examples of deployments of both traditional AI and generative AI (GenAI) with a lens on value. Results Traditional AI and GenAI are different technologies in terms of their capability and modes of current deployment, which have implications on value in health systems. Discussion Traditional AI when applied with a framework top-down can realize value in healthcare. GenAI in the short term when applied top-down has unclear value, but encouraging more bottom-up adoption has the potential to provide more benefit to health systems and patients. Conclusion GenAI in healthcare can provide the most value for patients when health systems adapt culturally to grow with this new technology and its adoption patterns.

Publisher

Oxford University Press (OUP)

Reference47 articles.

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