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 […]

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), […]

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 […]

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 […]

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 […]

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 […]

Practical templates for digital health ethics applications in Sweden: lessons from a sensor-based monitoring study

Obtaining ethical approval for digital health research involving vulnerable populations presents significant challenges for researchers, particularly when navigating complex regulatory frameworks like Sweden’s ethical review system. Despite official guidelines, researchers often struggle to translate general principles into concrete application documents that satisfy review authorities. This paper presents practical, reusable templates developed through the successful preparation […]

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