Tracking AI Usage in Healthcare UPDATE

Overview of Metrics for Tracking AI Models Full Diagrams: ORM, Barker, ERD at bottom of article. Monitoring AI performance across diverse training locations and varied health populations is an increasingly critical aspect of ensuring the robustness and inclusivity of AI models. Training location can significantly influence the quality of datasets used, as geographical, cultural, and…

Tracking AI Usage in Healthcare (part 3 of 3)

UW Health and Epic co-hosted a Roundtable on Artificial Intelligence in Healthcare on June 5, 2024, in Washington, DC. It aimed to explore AI’s role in healthcare delivery and patient/provider experience with thought leaders from various sectors. The discussions centered around: Dr. Micky Tripathi from ONC highlighted HHS initiatives and emphasized expanding FDA’s capacity to…

Tracking AI Usage in Healthcare (part 2 of 3)

Tracking population geography when training AI models for healthcare is highly beneficial as it allows the AI to understand and adapt to regional health trends and disparities. This geographical data ensures the AI models are tailored to provide accurate predictions and recommendations, reflecting the specific health needs and conditions of different populations. The relationship between…

Tracking AI Usage in Healthcare (part 1 of 3)

Tracking which AI models are used to support decisions in healthcare when patients are involved is crucial for ensuring accountability and transparency in medical practices. It enables healthcare providers to evaluate the effectiveness and accuracy of AI recommendations, ensuring that patient care is optimized and tailored to individual needs. An AI Agent can assist in…

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