Explainable, Fair, Reproducible and Collaborative Surgical Artificial Intelligence: Integrating data, algorithms and clinical reasoning for surgical risk assessment (XAI-IDEALIST)

项目摘要 在美国，平均每个美国人一生中要做七次外科手术。 每年有15万名外科病人死亡，150万人在手术后出现并发症。进展 医疗人工智能（AI）仍然因有限的数据集和模型而停滞不前，这些数据集和模型的可解释性不足， 透明度、公平性和可重复性，这些都很难在各机构间实现和共享。在 上一个资助期内，除了98篇出版物和3项专利，还实时智能手术风险 评估系统在佛罗里达大学成功实施。本报告的总体目标 续期申请是为了发展一个新的概念框架，以“可解释，公平，可再现， 协作医疗AI”为大规模临床实施提供基础。它将利用 一个佛罗里达，一个由22家医院组成的大型临床联盟，为全国第三大城市佛罗里达的1000万名患者提供服务。 最大的州。将通过三个具体目标实现总体目标。 (1)外部和前瞻性验证新的可解释性、动态性、可操作性、公平性和可重现性 实时手术风险监控的算法工具包。(2)开发和评估可解释的AI平台 （XAI-IDEALIST）使用基于人为的基准进行实时手术风险监测。(3)实施 并评估具有高级隐私功能的联合学习方法，以降低协作手术风险 模型训练这种方法是创新的，因为它代表了第一次尝试（1）建立第一个外科手术 FAIR（可查找、可解释、可互操作、可再现）AI就绪、大型多中心多模式数据集，（2） 新的计算方法伴随着评估公平性和可重复性，（3）多方面的 和全栈可解释的AI框架，以及（4）隐私保护模型的联邦学习能力 training训练across横过institutions机构.拟议的研究是重要的，因为它将解决几个关键问题， 关键障碍，包括（1）缺乏AI就绪的大型手术数据集，（2）缺乏可解释的，动态的， 可操作、公平和可重现的手术风险算法，（2）缺乏医疗AI可解释性平台，以及（4） 缺乏系统的方法来进行协作模式培训和机构间共享。最终 结果有望改善患者的预后，降低住院费用， 并发症

Project Summary In the United States, the average American can expect to undergo seven surgical operations during a 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. In the previous funding period, in addition to 98 publications and 3 patents, a real-time intelligent surgical risk assessment system was successfully implemented at University of Florida. The overall objective of this renewal application 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. (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. The approach is innovative, because it represents the first attempt to (1) build the first surgical FAIR (Findable, Accessible, Interoperable, Reproducible) AI-ready, large multicenter multimodal dataset, (2) Novel computational approaches accompanied by assessing fairness and reproducibility, (3) a multifaceted and full-stack explainable AI framework, and (4) federated learning capacity for privacy-preserving model trainingacross institutions. The proposed research is significant since it will address several key problems and critical barriers, including (1) lack of AI-ready large surgical datasets, (2) lack of interpretable, dynamic, actionable, fair and reproducible surgical risk algorithms, (2) lack of a medical AI explainability platform, and (4) 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.