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TRUVACE RECORD VERSION
record: TRV-2026-0879
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-25T06:05:48.500479Z
status: published
lens: trace
sector: health
headline: Overcoming the opaque side of AI in healthcare: a lifecycle based approach
dek: 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…
gain_title: 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_title: Existing practical approaches for implementing and evaluating transparency across the full lifecycle of AI-enabled medical devices are fragmented and lack systematic structure.
trace_subject: transparency implementation across the lifecycle of AI-enabled medical devices
gain_reading: 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.
gain_evidence: to support regulatory readiness, calibrated clinical trust, and safe real-world integration of AI in healthcare
problem_reading: Existing practical approaches for implementing and evaluating transparency across the full lifecycle of AI-enabled medical devices are fragmented and lack systematic structure.
problem_evidence: practical approaches to systematically operationalize transparency across the entire lifecycle of AI-enabled medical devices remain fragmented and insufficiently structured
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.
limitation: 
tag: Dual reading
key_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.
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:
- peer_reviewed | Expert Review of Medical Devices | https://doi.org/10.1080/17434440.2026.2723944 | 2026-08-24
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