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Performance of federated versus centralized learning for mammography classification across film–digital domain shift
IntroductionLarge, diverse datasets are essential for reliable deep learning in mammography, yet clinical data remain siloed due to privacy and governance constraints. Federated learning enables collaborative training without sharing raw data, but its robustness under strong imaging-domain heterogeneity, such as film–digital shifts, remains uncertain.MethodsWe conducted a comparative evaluation of centralized learning and cross-silo federated learning […]
Negotiating privacy and responsibility in digital public health: a qualitative study of the social and ethical implications of peer-to-peer health data sharing
IntroductionPeer-to-peer sharing of personal health data on social media is increasingly used as a strategy to support public health goals. Such sharing is often assumed to motivate individuals to adopt or maintain healthy behaviors. However, the social and ethical implications of sharing-based interventions remain insufficiently examined. This paper offers an empirical and theoretical contribution by […]
Explainable and reproducible AI: culturally responsive AI for health equity in minoritized groups
Artificial intelligence (AI) is transforming healthcare by enabling advanced diagnostics, personalized treatments, and improved operational efficiencies. By identifying complex data patterns and correlations, AI could supplement clinical decision-making, enabling more rapid diagnoses and treatment decisions tailored to meet the unique needs of diverse communities. However, realizing these benefits requires that clinical AI models be consistent, […]
Large language models in healthcare quality management: a European perspective on process automation and compliance
Large Language Models (LLMs) are transforming back-office quality management processes in European healthcare systems through automation of compliance monitoring, quality assurance, and process optimization without direct patient interaction. This narrative review synthesizes evidence from recent systematic reviews and implementation studies (2023-2025) examining LLM deployment within the European regulatory framework encompassing the Medical Device Regulation (MDR), […]
Prediction of maturity-onset diabetes of the young subtypes using machine learning
IntroductionMaturity-onset diabetes of the young (MODY) is a monogenic type of diabetes caused by different pathogenic genetic variants in glucose metabolism-related genes, with GCK-MODY and HFN1A-MODY subtypes being the most frequent. Diagnosing the specific MODY subtype is essential for correct treatment and follow-up, but it requires gene sequencing, a time-consuming and costly process that depends […]
Correction: Depression detection using deep learning and large language models from multimodalities
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Value, development challenges, and strategies for gaining internal endorsement of digitally connected subcutaneous drug delivery devices: a survey of pharmaceutical stakeholders
IntroductionConnected drug delivery devices such as combination products that integrate traditional drug delivery systems with digital connectivity features represent an opportunity to improve treatment outcomes and disease management. This online survey study was conducted to explore the evolving landscape of digitally connected subcutaneous (SC) drug delivery devices, including the perspectives of pharmaceutical stakeholders regarding the […]
Ethical oversight of AI-driven paediatric trials: a proactive, risk-sensitive interim review model
BackgroundArtificial intelligence (AI)-driven paediatric trials pose novel challenges for institutional review boards (IRBs), as traditional annual continuing review frameworks are often inadequate for evolving algorithmic and data-related risks. International and national regulations provide only limited guidance on how to design proactive, risk-sensitive interim oversight mechanisms for such research.ObjectiveTo develop and illustrate a risk-sensitive interim review […]
Early Type 2 diabetes risk prediction using explainable machine learning in a two-stage approach
BackgroundDiabetes is a chronic disease characterized by elevated blood glucose levels. Without early detection and proper management, it can lead to serious complications and increase healthcare costs. Its global prevalence is rising, with many cases remaining undiagnosed. In this study, we developed an explainable machine learning model using a two-stage approach for predicting diabetes.MethodsFive machine […]
Advancing the adoption of oncology decision support tools in Europe: insights from CAN.HEAL
Effective cancer care increasingly depends on digital decision support tools (DSTs) to interpret complex clinical, molecular, and genomic data and guide personalised treatment decisions. However, the oncology DST (oncDST) landscape remains fragmented, with limited interoperability, inconsistent standards, and uneven clinical adoption across healthcare systems. This fragmentation hinders routine clinical use and impedes the demonstration of […]
Enhanced meta ensemble stacking approach with XGBoost and optuna based detection of Parkinson’s disease
Parkinson’s disease (PD), a progressive neurological disorder affecting motor function, has been significantly rising in prevalence in recent years. Current diagnostic methods, relying on clinical observations, neurological exams, and periodical DaTscan imaging, may exhibit reduced sensitivity in the early stages. To develop a robust and multimodal machine learning model for early detection, an Ensemble Approach […]