Vincent Liu

This article synthesizes early cross-project insights from the 5 projects funded by the Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI), led by Kaiser Permanente and funded by the Gordon and Betty Moore Foundation and selected through a national, multistage review process using a structured scoring rubric, covering sepsis management, venous thromboembolism risk assessment, diabetic retinopathy screening, cardiac amyloidosis detection, and pediatric asthma risk prediction; it reports that real-world AI deployment was feasible across varied clinical environments, that common challenges included electronic health record integration, data complexity, regulatory requirements, and variation in clinical workflows, and that implementation success depends on thoughtful integra

This article synthesizes early cross-project insights from the 5 projects funded by the Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI), led by Kaiser Permanente and funded by the Gordon and Betty Moore Foundation and selected through a national, multistage review process using a structured scoring rubric, covering sepsis management, venous thromboembolism risk assessment, diabetic retinopathy screening, cardiac amyloidosis detection, and pediatric asthma risk prediction; it reports that real-world AI deployment was feasible across varied clinical environments, that common challenges included electronic health record integration, data complexity, regulatory requirements, and variation in clinical workflows, and that implementation success depends on thoughtful integra