While current AI technology is suitable for automating some repetitive clinical tasks, technical challenges remain in solving critical and gainful problems in the domains of patient and disease management. The proposed research seeks to address issues in medical AI, such as integrating medical knowledge effectively, making AI recommendations explainable to clinicians, and establishing safety guarantees.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Neurosymbolic Learning Algorithms
Timeframe: Prototype and develop new learning algorithms; 18 months. Benchmark and evaluate the learning algorithms; 24 months. Publish research results; 24 months
Explanation Methods
Timeframe: Prototype and develop new explanation algorithms; 18 months. Derive certified guarantees for explanations; 18 months. Benchmark and evaluate the explanation algorithms; 24 months. Extend certificates to new properties and tasks; 30 months. Publ
Methods for Safety Guarantees
Timeframe: Prototype and develop new rule learning algorithms; 30 months. Scale rule learning algorithms to larger data settings; 36 months. Incorporate new primitives to express complex rules; 36 months. Implement rule learning algorithms on baseline tasks
Haideliza Soto Calderon