AI Model Passport: Data and System Traceability Framework for Transparent AI in Health
Journal:
arXiv
Published Date:
Jun 27, 2025
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.
