XAI-IDEALIST
Project Introduction
The overall objective of this project is to develop a new conceptual framework for Explainable, Fair, Reproducible, and Collaborative Medical AI to provide a foundation for clinical implementation at scale. It will leverage the OneFlorida, a large clinical consortium of 22 hospitals serving 10 million patients in Florida, the nation’s third largest state. The overall objective will be achieved by pursuing three specific aims.
- External and prospective validation of novel interpretable, dynamic, actionable, fair and reproducible algorithmic toolkit for real-time surgical risk surveillance.
- Developing and evaluating explainable AI platform (XAI-IDEALIST) for real-time surgical risk surveillance using human-grounded benchmarks.
- Implementing and evaluating a federated learning approach with advanced privacy features for collaborative surgical risk model training.
The approach is innovative, because it represents the first attempt to
- Build the first surgical FAIR (Findable, Accessible, Interoperable, Reproducible) AI-ready, large multicenter multimodal dataset,
- Novel computational approaches accompanied by assessing fairness and reproducibility,
- A multifaceted and full-stack explainable AI framework, and
- Federated learning capacity for privacy-preserving model training across institutions.
The proposed research is significant since it will address several key problems and critical barriers, including
- lack of AI-ready large surgical datasets,
- lack of interpretable, dynamic, actionable, fair and reproducible surgical risk algorithms,
- lack of a medical AI explainability platform, and
- lack of a systematic approach for collaborative model training and sharing across institutions.
Ultimately, the results are expected to improve patient outcomes and decrease hospitalization costs, as well as lifelong complications.