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

2025-12-04

AI Summary

To address the lack of transparency and traceability in medical AI, this study proposes the first standardized AI Product Passport framework integrating the FUTURE-AI ethics guidelines with MLOps/ModelOps practices, specifically designed for the end-to-end lifecycle of heart failure prediction tools. Methodologically, we developed a modular, web-based platform built upon a relational data model that enables automated provenance tracking, while incorporating FHIR standards and FAIR principles to support dual-mode (machine-readable and human-interpretable) passport generation. Key contributions include: (1) the first structured medical AI passport model, encompassing four dimensions—purpose, data provenance, performance, and deployment context; and (2) an open-source, customizable platform (publicly released on GitHub), co-validated by 21 stakeholder groups, which significantly improves regulatory compliance and ethical auditability, achieving a 92% user satisfaction rate.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: AI for Accessibility
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

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.

Problem

• Develops a framework for AI transparency in healthcare
• Creates lifecycle documentation for AI models and data
• Ensures compliance with regulations and ethical standards

Innovation

• AI Product Passport framework for healthcare transparency
• Relational data model capturing AI lifecycle metadata
• Open-source platform with automated provenance tracking