Clinical AI model translation and deployment: creating a scalable, standardized, and responsible AI lifecycle framework in healthcare

David S. McClintock

doi:10.1117/12.3013913

3 April 2024 Clinical AI model translation and deployment: creating a scalable, standardized, and responsible AI lifecycle framework in healthcare

David S. McClintock

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129330G (2024) https://doi.org/10.1117/12.3013913
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

The use of artificial intelligence in healthcare is a current hot topic, generating tons of excitement and pushing multiple academic medical centers, startups, and large established IT companies to dive into clinical AI model development. However, amongst that excitement, one topic that has lacked direction is how healthcare institutions, from small clinical practices to large health systems, should approach AI model deployment. Unlike typical healthcare IT implementations, AI models have special considerations that must be addressed prior to moving them into clinical practice. This talk will review the major issues surrounding clinical AI implementations and present a scalable, standardized, and responsible framework for AI deployment that can be adopted by many different healthcare organizations, departments, and functional areas.

Conference Presentation

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David S. McClintock "Clinical AI model translation and deployment: creating a scalable, standardized, and responsible AI lifecycle framework in healthcare", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129330G (3 April 2024); https://doi.org/10.1117/12.3013913

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