arXiv 2512.137022025-12-04cs.CYcs.AI

提升医疗AI的透明度与可追溯性：AI产品护照

Enhancing Transparency and Traceability in Healthcare AI: The AI Product Passport

SRDC Software Research Development and Consultancy Corporation（SRDC软件研究开发与咨询公司）；Amsterdam University Medical Centre, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹大学医学中心）；University College London（伦敦大学学院）；Universitat de Barcelona（巴塞罗那大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚研究与高级研究机构（ICREA））

A. Anil Sinaci, Senan Postaci, Dogukan Cavdaroglu, Machteld J. Boonstra, Okan Mercan, Kerem Yilmaz, Gokce B. Laleci Erturkmen, Folkert W. Asselbergs, Karim Lekadir

备注：: A total of 33 pages: First 16 pages for the manuscript and the remaining 17 pages for the supplementary user guide of the graphical user interface

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本研究提出AI产品护照，通过生命周期文档提升医疗AI的透明度和可追溯性，符合FUTURE-AI原则，确保公平性和可用性，并通过开源平台实现可访问性。

摘要：

目标：开发AI产品护照，一种基于标准的框架，通过生命周期文档提高医疗AI的透明度、可追溯性和合规性。材料和方法：AI产品护照是在AI4HF项目中开发的，专注于心力衰竭AI工具。我们分析了监管框架（欧盟AI法案、FDA指南）和现有标准，设计了一个关系数据模型，捕捉AI生命周期各阶段的元数据：研究定义、数据集准备、模型生成/评估、部署/监控和护照生成。将MLOps/ModelOps概念整合以确保操作相关性。共同创建涉及AI4HF联盟的反馈以及在里斯本研讨会中21名不同利益相关者的意见，通过Mentimeter投票评估。开源平台使用Python库实现自动化可追溯性跟踪。结果：AI产品护照基于现有标准和方法设计，具有明确的生命周期管理和基于角色的访问权限。其实施是一个基于网页的平台，支持可追溯文档的关联数据模型。它生成机器可读和人工可读的报告，可定制用于不同利益相关者。它符合FUTURE-AI原则（公平性、普遍性、可追溯性、可用性、鲁棒性、可解释性），确保公平性、可追溯性和可用性。导出的护照详细说明模型目的、数据来源、性能和部署环境。托管在GitHub的后端/前端代码库增强了可访问性。讨论和结论：AI产品护照解决了医疗AI中的透明度缺口，满足了监管和伦理需求。其开源性质和与标准的契合增强了信任和适应性。未来改进包括FAIR数据原则和FHIR集成，以提高互操作性，促进负责任的AI部署。

英文摘要：

Objective: To develop the AI Product Passport, a standards-based framework improving transparency, traceability, and compliance in healthcare AI via lifecycle-based documentation. Materials and Methods: The AI Product Passport was developed within the AI4HF project, focusing on heart failure AI tools. We analyzed regulatory frameworks (EU AI Act, FDA guidelines) and existing standards to design a relational data model capturing metadata across AI lifecycle phases: study definition, dataset preparation, model generation/evaluation, deployment/monitoring, and passport generation. MLOps/ModelOps concepts were integrated for operational relevance. Co-creation involved feedback from AI4HF consortium and a Lisbon workshop with 21 diverse stakeholders, evaluated via Mentimeter polls. The open-source platform was implemented with Python libraries for automated provenance tracking. Results: The AI Product Passport was designed based on existing standards and methods with well-defined lifecycle management and role-based access. Its implementation is a web-based platform with a relational data model supporting auditable documentation. It generates machine- and human-readable reports, customizable for stakeholders. It aligns with FUTURE-AI principles (Fairness, Universality, Traceability, Usability, Robustness, Explainability), ensuring fairness, traceability, and usability. Exported passports detail model purpose, data provenance, performance, and deployment context. GitHub-hosted backend/frontend codebases enhance accessibility. Discussion and Conclusion: The AI Product Passport addresses transparency gaps in healthcare AI, meeting regulatory and ethical demands. Its open-source nature and alignment with standards foster trust and adaptability. Future enhancements include FAIR data principles and FHIR integration for improved interoperability, promoting responsible AI deployment.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。