Small, flexible, wireless device gently adheres to the chest to measure multiple physiological signals at once. By tracking heart rate and rhythm, breathing rate and depth, sweat response, blood flow and temperature, device captures a whole-body ‘view’ of stress. Machine learning algorithms analyze patterns associated with stress in real time. Device could help clinicians detect stress and discomfort in babies, the elderly and critically ill or sedated patients who cannot communicate.

Artificial intelligence models that automatically label organs and anatomical structures in medical images are increasingly used to study large public imaging datasets, but comparing these tools is difficult when no expert reference annotations exist. A recent study developed an open-source framework for evaluating and comparing AI-based anatomy segmentation models using inter-model agreement rather than ground truth. Applying the approach to six widely used segmentation models and chest CT scans from the National Lung Screening Trial, the researchers normalized all model outputs to follow standard lexicon, and stored the result in a common medical imaging format. Normalized representation made it possible to visualize and compare model outputs, and analyze how consistently each model segmented key structures such as the lungs, heart, ribs, vertebrae, and sternum. The results show strong agreement across models for lung segmentation, but substantial inconsistencies and systematic errors for bones and some cardiac structures, particularly among models trained on similar data. By combining quantitative agreement metrics with interactive visualization tools, the framework helps identify reliable models and flag problem cases, offering a practical way to guide model selection and large-scale reuse of AI-generated annotations.

A sweeping international study has identified ways to detect patients who are at serious risk of sudden death, heart failure or other dangerous outcomes from heart thickening known as hypertrophic cardiomyopathy.

Patients born with heart defects experience their first acute heart attack at a significantly earlier age than others. This is shown by a new national study from Lund University in Sweden. However, despite this earlier onset of illness, there is no difference in survival rates or the risk of further heart attacks between those with congenital heart defects and others.