Explainable, Fair, Reproducible, and Collaborative Surgical Artificial Intelligence: Integrating Data, Algorithms, and Clinical Reasoning for Surgical Risk Assessment

The average American can expect to undergo seven surgical operations during a lifetime. Each year in the United States, 150,000 surgical patients die and 1.5 million develop a complication after surgery. The risk for postoperative complications arises from the interaction between preoperative health and the complexity of care that the patient receives. Implementing preventive strategies that mitigate this risk depends on timely and accurate surgical risk surveillance throughout the continuum of perioperative care.

So far, the IDEALIST project has yielded 98 publications, 3 patents, and the successful implementation of a real-time intelligent surgical risk assessment system (MySurgeryRisk) at the University of Florida. Progress in medical artificial intelligence (AI), however, remains halted by limited datasets and models with insufficient interpretability, transparency, fairness, and reproducibility that are difficult to implement and share across institutions.

Figure 1. The MySurgeryRisk platform, part of the IDEALIST project, uses electronic health record data from one year prior to surgery along with real-time monitoring to predict the risk of postoperative complications.

The next objective of the IDEALIST project is to develop a new conceptual framework for “Explainable, Fair, Reproducible, and Collaborative Medical AI” in order to provide a foundation for clinical implementation at scale. IDEALIST leverages OneFlorida, a large clinical consortium of 22 hospitals serving 10 million patients in Florida, the nation’s third largest state. To achieve the next project objective, we are pursuing three specific aims.

  • External and prospective validation of a novel interpretable, dynamic, actionable, fair, and reproducible algorithmic toolkit for real-time surgical risk surveillance.
  • Development and evaluation of an explainable AI platform (XAI-IDEALIST) for real-time surgical risk surveillance using human-grounded benchmarks.
  • Implementation and evaluation of a federated-learning approach with advanced privacy features for collaborative surgical risk model training.

How could IDEALIST help patients?

IDEALIST could advance the ability of healthcare providers to diagnose diseases by computing the risk of postoperative complications in real-time. Ultimately, the results are expected to improve patient outcomes, decrease hospitalization costs, and reduce lifelong complications. The proposed research is relevant to public health because it can result in enhanced perioperative care workflow and early intervention.

Figure 2. The IDEALIST project aims to improve patient outcomes by optimizing the use of hospital resources and placing the most resources with the patients who need them.

Why is IDEALIST important to researchers?

The IDEALIST approach is innovative because it represents the first attempts 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
  • a 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 the lack of

  • AI-ready large surgical datasets,
  • interpretable, dynamic, actionable, fair and reproducible surgical risk algorithms,
  • a medical AI explainability platform, and
  • a systematic approach for collaborative model training and sharing across institutions.

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