Overcoming the opaque side of AI in healthcare: a lifecycle-based approach

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INTRODUCTION: Transparency has emerged as a foundational condition for trustworthy Artificial Intelligence (AI) in healthcare. Despite its centrality, practical approaches to systematically operationalize transparency across the entire lifecycle of AI-enabled medical devices remain fragmented and insufficiently structured. This work addresses this gap by proposing a lifecycle-oriented operational approach to guide the consistent implementation and evaluation of transparency in AI-based medical technologies. AREAS COVERED: A narrative synthesis of regulatory texts, international standards, and scientific literature related to software as a medical device (SaMD), the EU Medical Device Regulation (MDR), the EU Artificial Intelligence Act (AI Act), data-protection rules, and relevant ISO/IEC guidance. Using a SaMD lifecycle framework, we mapped transparency requirements to practical development, validation, and governance activities across ideation, data and design inputs, risk management, implementation/verification, technical and clinical validation, market placement, maintenance, and disposal. EXPERT OPINION: Transparency must be engineered as a lifecycle property, not an afterthought. We propose nine operational measures - covering documented design assumptions and datasets, transparency-oriented risk and change control, traceability, subgroup and independent validation, usability-based explainability, calibrated clinical evaluation, structured labeling, version-controlled updates, and regulated end-of-life data handling - to support regulatory readiness, calibrated clinical trust, and safe real-world integration of AI in healthcare.

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