A new study maps the rapidly evolving field of intelligent colonoscopy. It argues that the next leap will come not from isolated-task modeling alone, but from generalized multimodal systems that can perceive, describe, locate, and discuss findings in clinically useful language. To move the field forward, the researchers broadly reviewed 63 datasets and 137 deep-learning models spanning classification, detection, segmentation, and vision-language tasks.

Lung cancer remains the leading cause of cancer-related deaths worldwide, accounting for nearly one in five cancer deaths - around 1.8 million lives lost each year. One of the main reasons is late diagnosis: in its early stages, the disease appears as extremely small nodules that are difficult to distinguish from healthy tissue, even for experienced radiologists.

From hospital leaflets to spoken answers in dozens of languages, new research from the University of East London (UEL) suggests artificial intelligence (AI) could dramatically improve how patients learn about serious eye conditions.

A research team led by UEL’s Dr Mohammad Hossein Amirhosseini and Dr Fatima Kalabi from Queen’s Hospital in London, in collaboration with Moorfields Eye Hospital in London, and Inselspital University Hospital of Bern in Switzerland, has developed a multilingual, voice-enabled AI chatbot designed to help people understand retinal detachment

Accurate diagnosis in pediatric care can be particularly challenging, especially when rare diseases present with subtle or overlapping symptoms. Early uncertainty in diagnosis may delay treatment and increase the risk of complications. While artificial intelligence (AI) has shown potential in healthcare, most previous studies have relied on simplified or curated cases rather than real-world clinical data.

The model organ for this research project is the best pediatric brain tumor model developed so far and can be used to test new drugs. The results of the project, conducted by the University of Trento with Bambino Gesù Children's Hospital in Rome, were published in the Nature Protocols.

Neither radiologists nor multimodal large language models (LLMs) are able to easily distinguish artificial intelligence (AI)-generated “deepfake” X-ray images from authentic ones, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA). The findings highlight the potential risks associated with AI-generated X-ray images, along with the need for tools and training to protect the integrity of medical images and prepare health care professionals to detect deepfakes.

This study analyses the EU digital health market and economic impact of digital health technologies, including AI-enabled solutions.

This area is a priority workstream for the AI Office, reflecting its importance for AI innovation and competitiveness. The study Observatory for digital health technologies in Europe was commissioned by the Directorate-General for Communications Networks, Content and Technology (DG CONNECT).