Medical imaging articles from across Nature Portfolio

A new sensor that detects optoacoustic signals generated by mid-infrared light enables measurement of glucose concentration from intracutaneous tissue rich in blood. This technology does not rely on glucose measurements in interstitial fluid or blood sampling and might yield the next generation of non-invasive glucose-sensing devices for improved diabetes management.

A recent study reported the development and validation of the Liver Artificial Intelligence Diagnosis System (LiAIDS), a fully automated system that integrates deep learning for the diagnosis of liver lesions on the basis of contrast-enhanced CT scans and clinical information. This tool improved diagnostic precision, surpassed the accuracy of junior radiologists (and equalled that of senior radiologists) and streamlined patient triage. These advances underscore the potential of artificial intelligence to enhance hepatology care, although challenges to widespread clinical implementation remain.

This protocol describes the preparation of long-lasting aggregation-induced emission-based, near-infrared afterglow luminescence nanoprobes. Their enhanced afterglow intensity results in improved imaging sensitivity and depth in vivo.

By learning to pair dermatological images and related concepts in a self-supervised manner, a visual-language foundation model is shown to have comparable performance to supervised models for concept annotation and is used to scrutinize model decisions for enhanced interpretability and accountability of medical imaging applications.

A new sensor that detects optoacoustic signals generated by mid-infrared light enables measurement of glucose concentration from intracutaneous tissue rich in blood. This technology does not rely on glucose measurements in interstitial fluid or blood sampling and might yield the next generation of non-invasive glucose-sensing devices for improved diabetes management.

A recent study reported the development and validation of the Liver Artificial Intelligence Diagnosis System (LiAIDS), a fully automated system that integrates deep learning for the diagnosis of liver lesions on the basis of contrast-enhanced CT scans and clinical information. This tool improved diagnostic precision, surpassed the accuracy of junior radiologists (and equalled that of senior radiologists) and streamlined patient triage. These advances underscore the potential of artificial intelligence to enhance hepatology care, although challenges to widespread clinical implementation remain.

Growth chart studies of the human cerebellum, which is increasingly recognized as pivotal for cognitive development, are rare. Gaiser and colleagues utilized population-level neuroimaging to unveil cerebellar growth charts from childhood to adolescence, offering insights into brain development.

We explore the evolving landscape of diagnostic artificial intelligence (AI) in dermatology, particularly focusing on deep learning models for a wide array of skin diseases beyond skin cancer. We critically analyze the current state of AI in dermatology, its potential in enhancing diagnostic accuracy, and the challenges it faces in terms of bias, applicability, and therapeutic recommendations.

New findings provide insight into the natural history of subclinical synovitis, a reported predictor of the development of rheumatoid arthritis, and identify various factors associated with its reversal.