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手术。 每年有15万名手术患者死亡，150万人在手术后出现并发症。取得的进展 医学人工智能(AI)仍然因为有限的数据集和解释不足的模型而停滞不前， 透明度、公平性和再现性，这些都是难以实施和跨机构共享的。在 此前资助期内，除98项出版物和3项专利外，还有一项实时智能手术风险 该评估体系已在佛罗里达大学成功实施。这样做的总体目标是 续订申请是为了开发一个新的概念性框架，以实现可解释、公平、可重现和 协作式医疗AI“，为规模化的临床实施提供基础。它将利用 One佛罗里达，一个由22家医院组成的大型临床联盟，为佛罗里达州的1000万患者提供服务，这是全国第三大临床联盟 最大的州。总体目标将通过追求三个具体目标来实现。 (1)新的可解释、动态、可操作、公平和可复制的外部和预期验证 用于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-理想主义者)，使用人类接地基准进行实时手术风险监测。(三)落实 以及针对协作手术风险评估具有高级隐私特征的联合学习方法 模特儿训练。这种方法是创新的，因为它代表了(1)建造第一个外科手术的第一次尝试 公平(可查找、可访问、可互操作、可重现)支持人工智能的大型多中心多模式数据集，(2) 伴随着评估公平性和重复性的新的计算方法，(3)多方面的 和全栈可解释人工智能框架，以及(4)隐私保护模型的联合学习能力 跨机构培训。拟议的研究具有重要意义，因为它将解决几个关键问题和 关键障碍，包括(1)缺乏支持人工智能的大型外科数据集，(2)缺乏可解释的、动态的、 可操作、公平和可重复的手术风险算法，(2)缺乏医疗AI可解释平台，以及(4) 缺乏一种系统的方法，以便在各机构之间进行协作模式培训和分享。归根结底， 预计结果将改善患者的预后，降低住院费用，以及终生 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.