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This course focuses on the lessons learned from evaluating and debugging healthcare AI in deployment. The learning outcomes include understanding data curation, model testing and monitoring, and designing AI for optimizing clinician's performance. The course teaches skills such as quantifying data contributions to model success, explaining model mistakes, and improving fairness using Data Shapley. The teaching method involves real-time trials, analyzing FDA-approved medical AI systems, and discussing insights and challenges. The intended audience for this course includes professionals in the healthcare and artificial intelligence fields looking to deploy AI systems effectively.
