KiMeKo: A Collaborative AI Platform for Medical Device Development

Artificial intelligence-based software as a medical device: regulatory evolution toward continuous oversight

This structured narrative review examines how AI-SaMD regulation is moving beyond single-point premarket evaluation toward continuous and dynamic oversight, and synthesizes five evidence dimensions central to ongoing regulatory assurance: data evidence, algorithm evidence, software and cybersecurity evidence, clinical scenario and human factors evidence, and real-world data/real-world evidence with change management.

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

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.

Laboratory medicine 2050: from testing to health intelligence network, a vision from the IFCC Emerging Technologies Division

Ten megatrends that may shape laboratory medicine by mid-century are explored, including precision multi-omics, AI-supported diagnostics, distributed healthcare models, patient-owned data ecosystems, digital twins, convergence of imaging and laboratory medicine, population-wide prevention strategies, regenerative therapies, sustainability imperatives, and workforce transformation.

Human factors engineering in intelligent medical devices: paradigm evolution, core issues and frontier prospects

Against the ongoing intelligent transformation of global healthcare, human factors engineering (HFE) is essential to the safe deployment of intelligent medical devices. Yet existing reviews largely offer chronological summaries and fragmented, scenario-specific analyses, leaving the field’s paradigm evolution and cross-scenario human–AI challenges under-synthesized. Following the PRISMA-ScR guideline, this scoping review synthesizes the literature to construct a four-stage developmental framework for medical HFE, delineate four interconnected core human–machine collaboration challenges, and clarify the prevailing theoretical, methodological, and translational constraints. It further identifies future directions—human–AI collaborative reliability evaluation, data-driven usability testing, and digital-twin-supported full-lifecycle validation. By integrating paradigm evolution, core challenges, and regulatory translation into a single analytical framework, this work provides a structured foundation for subsequent research, human-centered design, and regulatory evaluation of intelligent medical devices.

PROACTIVE-AI: An Intelligent Dashboard for Assessing and Predicting of FDA-approved AI Software Performance in Real-World Clinical Scenarios

Recent years have witnessed a surge in FDA approved AI tools for healthcare applications. While this growth offers considerable potential benefits for clinical practice, it also introduces substantial challenges related to ethics, regulation, and patient safety. These challenges are further compounded by previously documented gaps in the regulatory approval pathway. These gaps include inconsistent pre-market evaluation practices, over-reliance on retrospective studies, and the limited systematic post-market surveillance of AI devices in real-world clinical settings. Using publicly available FDA data, we developed therefore an interactive web-based dashboard for assessing and predicting the performance of FDA-approved AI software, called PROACTIVE-AI, for the purpose of pro-viding the user with a structured guidance on the anticipated performance of AI-enabled medical devices in real-world clinical settings. The dashboard supports exploratory analysis by diverse stakeholders via knowledge graph visualization and longitudinal trend monitoring of performance indicators, including device recalls and safety-related issues. In addition, PROACTIVE-AI incorporates an AI-aided post-market surveillance risk assessment calculator, derived from historical recall data, to identify device characteristics and con-textual factors associated with elevated deployment risk. Our findings using the PROACTIVE-AI dashboard highlight some of the important challenges related to real-world monitoring and accountability of deployed AI medical devices. Furthermore, it illustrates the potential value of such dashboard in narrowing the trust gap surrounding AI in healthcare by providing quantitative metrics of expected clinical performance and recall-related risk factors.

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