What does this research mean for the field?

Transparency in AI-enabled medical devices must be systematically engineered as a lifecycle property through structured operational measures across development, validation, and governance to ensure regulatory readiness and clinical trust. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To establish a lifecycle-oriented operational framework that guides the systematic implementation, evaluation, and governance of transparency in AI-enabled medical devices.

August 26, 2026Expert Review of Medical Devices

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

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Authors

EBElisabetta Bianchini LBLucia Billeci NCNoemi Conditi

Discussion

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Overview

Narrative review reveals a lifecycle framework for medical AI devices, highlighting nine operational measures to ensure transparency, regulatory compliance, and safe clinical adoption.

Key Points

To establish a lifecycle-oriented operational framework that guides the systematic implementation, evaluation, and governance of transparency in AI-enabled medical devices.
Synthesized regulatory frameworks (EU MDR, EU AI Act), data-protection legislation, and ISO/IEC standards governing software as a medical device (SaMD).
Mapped transparency requirements across the complete SaMD lifecycle, including ideation, design, risk management, technical/clinical validation, market launch, maintenance, and disposal.
Demonstrated that transparency must be engineered proactively as a continuous lifecycle property rather than an isolated post-market evaluation.
Defined nine operational measures including documented dataset assumptions, traceability, subgroup validation, usability-focused explainability, structured labeling, and regulated end-of-life data handling.

Cite This Study

Bianchini et al. (2026) studied this question.

synapsesocial.com/papers/6a8e9bcc451774b83f3b49b9 https://doi.org/10.1080/17434440.2026.2723944

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August 26, 2026Expert Review of Medical Devices

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

View Full Paper

Ask AI

Bookmark

Authors

EBElisabetta Bianchini LBLucia Billeci NCNoemi Conditi

Discussion

Member takes

Overview

Narrative review reveals a lifecycle framework for medical AI devices, highlighting nine operational measures to ensure transparency, regulatory compliance, and safe clinical adoption.

Key Points

To establish a lifecycle-oriented operational framework that guides the systematic implementation, evaluation, and governance of transparency in AI-enabled medical devices.
Synthesized regulatory frameworks (EU MDR, EU AI Act), data-protection legislation, and ISO/IEC standards governing software as a medical device (SaMD).
Mapped transparency requirements across the complete SaMD lifecycle, including ideation, design, risk management, technical/clinical validation, market launch, maintenance, and disposal.
Demonstrated that transparency must be engineered proactively as a continuous lifecycle property rather than an isolated post-market evaluation.
Defined nine operational measures including documented dataset assumptions, traceability, subgroup validation, usability-focused explainability, structured labeling, and regulated end-of-life data handling.

Cite This Study

Bianchini et al. (2026) studied this question.

synapsesocial.com/papers/6a8e9bcc451774b83f3b49b9 https://doi.org/10.1080/17434440.2026.2723944

View Full Paper

Ask AI

Bookmark