Healthcare AI can no longer be a black box

Policymakers across Europe and the United States are moving to classify many clinical AI tools as high-risk systems, requiring developers and deployers to demonstrate transparency, traceability, and ongoing monitoring. In practice, however, healthcare organizations often rely on fragmented documentation that fails to capture how data were processed, how models evolved over time, or how updates affected performance after deployment.

How can hospitals, developers, and oversight bodies truly understand how an AI system was built, trained, updated, and deployed when patient safety is at stake? A new international research effort argues that without full lifecycle transparency, healthcare AI risks losing public trust and regulatory legitimacy.

In a study titled Enhancing Transparency and Traceability in Healthcare AI: The AI Product Passport, published as a preprint research paper, a multidisciplinary team of researchers introduces a standards-based framework designed to document healthcare AI systems from conception to real-world use. The study presents the AI Product Passport as a practical response to growing regulatory pressure under frameworks such as the European Union AI Act and U.S. FDA guidance for software used as a medical device, both of which demand stronger accountability but offer limited operational detail.

Why healthcare AI transparency has become a regulatory priority

Policymakers across Europe and the United States are moving to classify many clinical AI tools as high-risk systems, requiring developers and deployers to demonstrate transparency, traceability, and ongoing monitoring. In practice, however, healthcare organizations often rely on fragmented documentation that fails to capture how data were processed, how models evolved over time, or how updates affected performance after deployment.

The researchers identify transparency gaps as a structural weakness in current healthcare AI practice. Many systems rely on complex machine learning models trained on heterogeneous data sources such as electronic health records, imaging repositories, registries, and wearable devices. These data streams are difficult to reconstruct retrospectively, especially when models are updated or retrained. As a result, clinicians and regulators may have limited visibility into the origins of predictions, the presence of bias, or the context in which a model should or should not be used.

Existing tools address only parts of this problem. Model Cards summarize model behavior and intended use. FactSheets document supplier claims and performance benchmarks. Provenance standards track data lineage within machine learning pipelines. Operational frameworks such as MLOps and ModelOps manage version control and deployment. The study finds that while each approach adds value, none provides a unified, end-to-end solution tailored to the strict accountability requirements of healthcare.

The AI Product Passport is proposed as a way to bridge this gap. Rather than introducing an entirely new standard, the framework integrates established methods into a single, lifecycle-based documentation system. The aim is to make transparency operational, not symbolic, by embedding documentation into everyday AI development and deployment workflows.

How the AI Product Passport tracks models across the full lifecycle

Under the hood, the framework is a structured data model that records how an AI system changes over time. The study divides the AI lifecycle into five connected phases: study definition, dataset and feature preparation, model generation and evaluation, deployment and monitoring, and final passport generation. Each phase captures both technical metadata and contextual information, creating a continuous audit trail that can be reviewed long after a model enters clinical use.

During the study definition phase, the passport records the purpose of the AI system, the clinical context in which it will operate, the target population, and ethical or governance considerations. This establishes a clear point of origin and defines the boundaries of intended use. By documenting these assumptions early, the framework helps prevent later misuse or scope drift.

The dataset and feature preparation phase captures where data come from, how they are cleaned and transformed, and how training, validation, and test sets are constructed. Provenance tracking is used to document every transformation step, enabling traceability from raw clinical data to model-ready inputs. This is especially important in healthcare, where data quality and representativeness directly affect patient outcomes.

Model generation and evaluation are documented with equal rigor. The passport records the algorithms used, the software implementations, parameter settings, training procedures, and evaluation metrics. It also captures model limitations and known risks. This information is essential for clinicians and regulators who need to understand not just how accurate a model is, but under what conditions it performs reliably.

Once deployed, the passport continues to evolve. Deployment and monitoring records describe where the model is running, whether in the cloud, on local servers, or at the edge, and how its performance is monitored in real-world conditions. Updates, retraining events, and version changes are logged to ensure that post-deployment behavior can be traced back to specific design decisions.

In the final phase, all accumulated metadata are compiled into an AI Product Passport that can be exported in both machine-readable and human-readable formats. The level of detail can be adjusted depending on the audience, allowing regulators, developers, clinicians, and auditors to access the information most relevant to their role.

From ethical principles to operational trust in clinical AI

The AI Product Passport is explicitly designed around the FUTURE-AI principles for trustworthy healthcare AI, which emphasize fairness, universality, traceability, usability, robustness, and explainability. Rather than treating these principles as abstract ideals, the framework translates them into concrete documentation practices.

Fairness is supported by making training data characteristics and evaluation outcomes visible, helping stakeholders identify potential biases. Universality is addressed through flexible reporting that adapts to different levels of technical expertise. Traceability is achieved through lifecycle-based provenance capture. Usability is enhanced by role-based access and simplified reporting interfaces. Robustness is supported by documenting training conditions and deployment contexts. Explainability is strengthened by clearly describing model inputs, parameters, and outputs.

The study also highlights the importance of collaboration across roles. The passport is designed to be maintained collectively by study owners, data engineers, data scientists, machine learning engineers, and quality assurance specialists. Each role contributes to specific lifecycle phases, creating shared responsibility for transparency rather than placing the burden on a single team.

By releasing the framework as open source, the authors aim to encourage adoption and adaptation across healthcare systems. The inclusion of a Python library that automates metadata capture during model development is intended to reduce the manual effort that often discourages thorough documentation. This automation helps ensure that transparency keeps pace with rapid model iteration, rather than becoming an afterthought.

Looking ahead, the study outlines future enhancements, including alignment with FAIR data principles and integration with healthcare interoperability standards. These steps are intended to improve discoverability, reuse, and cross-institutional collaboration while maintaining strict governance controls.