112-OR: Mitigating Adoption Bias in Medical Autonomous AI—Quantifying the Balance between Accuracy and Access

Abstract

Introduction and Objective: Population Achieved Sensitivity (PAS) assumes that the primary goal of a diagnostic process is to identify patients who can benefit from intervention. We used PAS to analyze adoption bias — characterized by inequitable adoption of AI technologies — for an autonomous AI for diabetic eye exams. Methods: We compared two autonomous AI algorithms paired with a desktop fundus camera and a handheld retina camera. Sensitivity for the preregistered clinical trials were reported (NCT02963441 and NCT05808699) and the PAS formula was derived from an ethical framework as presented in npj Digital Medicine - Nature. Access was estimated from the numbers of desktop fundus cameras and handheld retina cameras deployed in US primary care settings. The heatmap presents PAS values for any level of access (0-100% penetrance) and sensitivity ≥ 60%. Results: PAS increases with increasing access and/or sensitivity. The top-right quadrant shows the highest PAS value. Though the handheld retina camera has slightly lower sensitivity (82%) compared with the desktop fundus camera (87%), its greater potential for adoption (estimated at 10X) shows increased detection of diabetic retinal disease in real-world settings. Conclusion: Mitigating adoption bias requires balancing accuracy and access, quantifiable through PAS. This example illustrates how to achieve this balance, empowering clinicians to focus on both diagnostic accuracy and access. Disclosure R. Channa: None. C. Joyce: Consultant; Digital Diagnostics. M.D. Abràmoff: Stock/Shareholder; Digital Diagnostics. Board Member; Digital Diagnostics. Consultant; Digital Diagnostics.

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