Jul 2026· Annual International Computer Software and Applications Conference· pp. 1669-1672· 0 citations· 9 references
Computer Science
TL;DR
The project contributes a practical toolchain and platform capabilities for non-experts and experts, including structured innovation support, uncertaintyaware sensor-data fusion, hybrid expert-system modeling, and workflow-guided data acquisition and anonymization.
Abstract
KiMeKo (KI-Med-Kollaborationsplattform) is a publically funded collaborative research project that develops a sustainable AI-Med ecosystem for AI-based medical device development. The project runs from July 2024 to December 2027 and joins seven Northern German research institutions. KiMeKo addresses the complete development trajectory, from concept and data acquisition to validation, regulatory evidence generation, and approval-oriented documentation. The project contributes a practical toolchain and platform capabilities for non-experts and experts, including structured innovation support, uncertaintyaware sensor-data fusion, hybrid expert-system modeling, and workflow-guided data acquisition and anonymization. This paper summarizes project objectives, expected outputs, relevance to IEEE COMPSAC 2026 themes, and current progress. In particular, KiMeKo aligns with Applied AI and Smart & Connected Health by combining AI engineering, privacy-conscious data processing, and regulation-aware medical software development.
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...
Yukun Dong, Ping Jiang, Xiao-Hua Zhou· Medical Review· 0 citations
This work proposes nine operational measures - covering 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...
E. Bianchini, L. Billeci, Noemi Conditi et al.· Expert Review of Medical Dev...· 0 citations
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 thera...
Damien Gruson, Bernard Gouget, Woo-Chang Lee et al.· Clinical Chemistry and Labor...· 1 citation
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.
Hui-Ling Hu, Ran Li, Yong Yin et al.· Frontiers in Industrial Engi...· 0 citations
An interactive web-based dashboard for assessing and predicting the performance of FDA-approved AI software, called PROACTIVE-AI, is developed for the purpose of pro-viding the user with structured guidance on the anticipated performance of AI-enabled medical devices in real-world clinical settings.
Isadora Oliveira Grasel, Naveena Gorre, I. El Naqa· Research Square· 0 citations
This review evaluates the current state of AI applications in medicine, focusing on clinical knowledge encoding, molecular drug discovery, and administrative workflow optimization, while critically addressing the technical, ethical, and systemic challenges of their institutional implementation.
Aleksandra Stańczyk, Kinga Haduch, Zuzanna Michalska et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
