标题： 推动负责任、安全和可持续的医疗AI - 临床和运营影响的战略框架 显示英文标题

标题： Enabling Responsible, Secure and Sustainable Healthcare AI - A Strategic Framework for Clinical and Operational Impact

摘要： 我们提供一个务实的模型，以实现负责任、安全和可持续的医疗AI，将世界级的技术卓越与组织准备度相一致。 该框架包括五个关键支柱——领导力与战略、MLOps与技术基础设施、治理与伦理、教育与人才发展，以及变革管理与采纳——旨在实现“设计合规性”同时产生可衡量的影响。 我们通过两个部署来展示其效用。 (A) 住院患者住院时间（LOS）预测服务在观察性试点中验证队列的R^2=0.41-0.58（n = 3,184次就诊，4个单元，2025年6月至8月）。 第6周的采纳率为78%，目标单元在复杂病例中的平均LOS相比试点前基线下降了5-10%。 (B) 一种AI增强的肺结节放射学第二读者（与PACS集成，具有阈值和解释叠加功能）实现了高敏感性（95%），并在检测亚厘米可操作性发现方面提高了+8.0个百分点的检测率，而不会减慢工作流程（中位报告TAT为23分钟，p = 0.64）。 这两个服务都在受监控、可审计的管道中执行，具有明确的回滚、偏差检查，且没有安全事件的证据。 这些发现表明，通过将强大的MLOps和AI安全与治理、教育及以人为本的变革相结合，我们可以加速AI的采用，同时提高安全性和结果。 最后，我们讨论了局限性、泛化考虑因素以及在各种临床和运营使用案例中扩展的路线图。 显示英文摘要

摘要： We offer a pragmatic model to operationalize responsible, secure, and sustainable healthcare AI, aligning world-class technical excellence with organizational readiness. The framework includes five key pillars - Leadership & Strategy, MLOps & Technical Infrastructure, Governance & Ethics, Education & Workforce Development, and Change Management & Adoption - and is intended to operationalize 'compliance-by-design' while delivering measurable impact. We demonstrate its utility through two deployments. (A) An inpatient length of stay (LOS) prediction service had R^2=0.41-0.58 with validation cohorts in an observational pilot (n = 3,184 encounters, 4 units, June-August 2025). Adoption was 78 percent by week 6, and target units saw 5-10 percent relative declines in mean LOS for complex cases vs. pre-pilot baselines. (B) An AI-augmented radiology second-reader for lung nodules (PACS-integrated with thresholding and explanation overlays) achieved high sensitivity (95 percent) and provided a +8.0 percentage-point lift in detection of sub-centimeter actionable findings, without slowing workflow (median report TAT 23 min, p = 0.64). Both services executed in monitored, auditable pipelines with well-defined rollback, bias checks, and no evidence of security incidents. These findings indicate that by combining strong MLOps and AI security with governance, education, and human-centric change, we can accelerate adoption of AI while improving security and outcomes. We end with limitations, generalization considerations, and a roadmap for scaling across varied clinical and operational use cases.