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Health·The Trace·Dual reading·Published 2026-08-25

transparency implementation across the lifecycle of AI-enabled medical devices

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

Abstract: 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…

TRV-2026-0879Peer-reviewedPermanent record — cite & verify
Trace impact reading

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P 68The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 66The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
Overcoming the opaque side of AI in healthcare: a lifecycle based approach

Jaturawit Rodcheewan at Manila Doctors Hospital by B20180. CC0 · http://creativecommons.org/publicdomain/zero/1.0/deed.en

The quick read

Published August 2026, this peer-reviewed synthesis addresses transparency as a foundational condition for trustworthy AI in healthcare. It finds current methods to operationalize transparency across AI-enabled medical devices are fragmented, and proposes a SaMD lifecycle framework to map regulatory and standards requirements to concrete development and governance steps.

The work matters because it shifts transparency from an afterthought to an engineered lifecycle property intended to support regulatory readiness and safe clinical use. What remains uncertain is whether the nine proposed measures improve actual clinical trust or safety outcomes, as the article presents a conceptual operational approach rather than empirical validation.

Main points
  • Article synthesizes EU MDR, EU AI Act, data-protection rules, and ISO/IEC guidance for software as a medical device.
  • Maps transparency requirements to SaMD lifecycle stages from ideation and data inputs through validation, market placement, maintenance, and disposal.
  • Lists nine operational measures including documented design assumptions and datasets, traceability, subgroup and independent validation, and version-controlled updates.
Gain

A lifecycle-oriented operational framework with nine measures was proposed to engineer transparency into AI-enabled medical devices to improve regulatory readiness and calibrated clinical trust.

Problem

Existing practical approaches for implementing and evaluating transparency across the full lifecycle of AI-enabled medical devices are fragmented and lack systematic structure.

The rundown

The authors conducted a narrative synthesis of regulatory texts, standards, and literature covering SaMD, EU MDR, EU AI Act, data-protection rules, and ISO/IEC guidance, then mapped requirements to development, validation, and governance activities.

The proposed operationalization spans ideation, data and design inputs, risk management, implementation/verification, technical and clinical validation, market placement, maintenance, and disposal, including structured labeling and regulated end-of-life data handling.

Sources

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