AI Model Passport: Data and System Traceability Framework for Transparent AI in Health

2025-06-27

AI Summary

Current medical AI systems lack scalable, verifiable, and machine-readable governance frameworks, resulting in low transparency, poor traceability, limited cross-platform interoperability, and non-unique model identification. To address these challenges, we propose the “AI Model Passport” framework—a novel approach that assigns standardized, cryptographically verifiable, globally unique digital identities to medical AI models. The framework enables automated, lifecycle-aware metadata collection, versioned model management, and decoupling of operational logic from configuration scripts. Built upon an MLOps architecture, it supports heterogeneous development environments and integrates seamlessly with existing clinical AI workflows. Evaluated on the ProCAncer-I dataset, the framework significantly improves model reproducibility and regulatory compliance, reduces reliance on manual documentation, and enhances transparency, trustworthiness, and cross-system interoperability.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: AI for Accessibility
• Machine Learning: Transparent, Interpretable, Explainable 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

The increasing integration of Artificial Intelligence (AI) into health and biomedical systems necessitates robust frameworks for transparency, accountability, and ethical compliance. Existing frameworks often rely on human-readable, manual documentation which limits scalability, comparability, and machine interpretability across projects and platforms. They also fail to provide a unique, verifiable identity for AI models to ensure their provenance and authenticity across systems and use cases, limiting reproducibility and stakeholder trust. This paper introduces the concept of the AI Model Passport, a structured and standardized documentation framework that acts as a digital identity and verification tool for AI models. It captures essential metadata to uniquely identify, verify, trace and monitor AI models across their lifecycle - from data acquisition and preprocessing to model design, development and deployment. In addition, an implementation of this framework is presented through AIPassport, an MLOps tool developed within the ProCAncer-I EU project for medical imaging applications. AIPassport automates metadata collection, ensures proper versioning, decouples results from source scripts, and integrates with various development environments. Its effectiveness is showcased through a lesion segmentation use case using data from the ProCAncer-I dataset, illustrating how the AI Model Passport enhances transparency, reproducibility, and regulatory readiness while reducing manual effort. This approach aims to set a new standard for fostering trust and accountability in AI-driven healthcare solutions, aspiring to serve as the basis for developing transparent and regulation compliant AI systems across domains.

Problem

• Lack scalable frameworks for transparent AI in health systems
• Missing verifiable identity for AI models across use cases
• Insufficient metadata for AI model lifecycle traceability

Innovation

• AI Model Passport for digital identity and verification
• Automated metadata collection with AIPassport tool
• Structured framework for lifecycle transparency and traceability