Subgroups – 2.3 | Monitoring & Optimization – AIiHC ORG

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2.3 | Monitoring & Optimization

Public AI Adoption Cycle

Public AI Adoption Cycle

Active 3 months ago

Purpose: For ensuring the ongoing performance, safety, value, and continuous improvement of deployed... View more

Public AI Adoption Cycle

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Group Description

Purpose: For ensuring the ongoing performance, safety, value, and continuous improvement of deployed AI systems in healthcare.

Description: This group focuses on the post-implementation phase of AI adoption, addressing how to continuously track, evaluate, refine, and maintain AI solutions to ensure they remain effective, safe, and valuable over time.

Intended Use: For discussing methodologies for monitoring AI model drift, evaluating real-world ROI, managing updates, and ensuring the long-term reliability and ethical performance of deployed AI systems.

Limitations: Not for initial implementation planning (2.2) or AI model development (1.3). Focus is on post-deployment management and refinement.

Key Activities:
Defining and tracking key performance indicators (KPIs) for deployed AI.
Discussing strategies for detecting and addressing AI model drift.
Planning for continuous feedback loops and iterative improvements for AI solutions.
Establishing protocols for post-deployment surveillance and safety monitoring.

Potential Users: Healthcare Leader/Administrator, IT & Infrastructure Professional, AI Innovator/Developer, Clinician/Practitioner.

Possible Discussions: “How do we monitor the long-term accuracy of our AI diagnostic tool?”, “Strategies for continually improving AI performance based on real-world data,” “What are the ethical considerations in post-deployment AI monitoring?”

Other Important Notes: This group addresses the critical challenge of maintaining AI efficacy and safety beyond initial deployment.

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