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), […]
Correction: Depression detection using deep learning and large language models from multimodalities
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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 […]
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 […]
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 […]
Depression subtype classification from social media posts: few-shot prompting vs. fine-tuning of large language models
BackgroundSocial media provides timely proxy signals of mental health, but reliable tweet-level classification of depression subtypes remains challenging due to short, noisy text, overlapping symptomatology, and labeling bias. Large language models (LLMs) are increasingly used in mental health for tasks such as symptom extraction, risk screening, and triage, yet their reliability for fine-grained depression subtype […]
Here’s why some people choose cryonics to store their bodies and brains after death
This week I reported on some rather unusual research that focuses on the brain of L. Stephen Coles. Coles was a gerontologist who died from pancreatic cancer in 2014. He had spent the latter part of his career specializing in human longevity. And before he died, he decided to have his brain preserved by a […]
Structuring integration for patient-centered care: a review-informed ontology-driven modular front-end framework for digital health innovation
BackgroundSemantic interoperability remains a significant barrier in healthcare, particularly when integrating patient-reported, clinical, and genomic data to enable personalized care. Existing models rarely focus on patient-centered, ontology-driven front-end architectures based on widely adopted standardized medical ontologies and terminologies. Within broader Personal Health Data Space (PHDS) initiatives, such integration increasingly depends on front-end frameworks that enable […]
AI-enabled cardiovascular devices: a lifecycle playbook for evidence, change control, and post-market assurance
AI-enabled cardiovascular devices are increasingly used in imaging, physiological signal analysis, and clinical decision support systems. Despite growing clinical adoption, requirements for evidence generation, software change management, and post-deployment assurance remain fragmented across jurisdictions and are often difficult to translate into operational processes within healthcare organizations. This review synthesizes common foundations of software as a […]
Chatbots as frontline educators in sexual reproductive health rights: evidence, limitations, and ethical considerations
Chatbots are increasingly used in digital health to expand access to information and support user engagement. In sexual and reproductive health and rights (SRHR), where stigma, privacy concerns, and health system constraints often limit timely access to accurate information, chatbots have been proposed as scalable tools for delivering education and facilitating service navigation. This perspective […]