Researchers at Penn State developed a new class of 3D-printed bioelectronics made of soft, stretchy materials, as well as an adhesive component that helps the device painlessly stick to biological tissue. The team reported that their new design, which they call CaroFlex and tested in a rodent model, relieved hypertension while causing much less damage to surrounding tissue.

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.