Project Summary In the United States, the average American will undergo seven surgical operations during their lifetime. Each year 150,000 surgical patients die, and 1.5 million develop a complication after surgery. Progress in medical Artificial Intelligence (AI) remains halted by limited datasets and models with insufficient interpretability, transparency, fairness, and reproducibility that are difficult to implement and share across institutions. The overall objective of this administrative equipment supplement is to develop a new conceptual framework that includes digital pathological images from surgical biopsies for “Explainable, Fair, Reproducible, and Collaborative Medical AI”. The widespread adoption of electronic health records (EHR) leading to digital data has enabled the proliferation of AI/ML tools for risk surveillance and diagnosis. However, progress in the field remains halted by limited datasets (such as lack of digital pathological images) and models with insufficient interpretability, fairness, and reproducibility that are difficult to implement and share across institutions. The lack of explainability and transparency of black-box AI models is challenging for the high-risk medical environment and has delayed clinical implementation of AI on a large scale. The overall objective will be achieved by incorporating digital pathology images into three specific aims: (1) External and prospective validation of novel interpretable, dynamic, actionable, fair and reproducible algorithmic toolkit for real-time surgical risk surveillance. (2) Developing and evaluating explainable AI platform (XAI-IDEALIST) for real-time surgical risk surveillance using human-grounded benchmarks. (3) Implementing and evaluating a federated learning approach with advanced privacy features for collaborative surgical risk model training. Ultimately, the results are expected to improve patient outcomes and decrease hospitalization costs, as well as lifelong complications. With the requested equipment, Leica Versa 200 we will aim to scan 500 pathology slides per week for our retrospective data and approximately 3700 slides that will be used during our validation stage. Additional institutional resources provided by the University of Florida College of Medicine Office of Research will pay for a trained technician, equipment maintenance, and digital storage fees for the life of the grant.