Artificial intelligence (AI) may be able to reveal how fast your body is aging by analyzing a chest X-ray, according to a new study published in The Journals of Gerontology. Researchers found that a deep learning model was able to detect subtle, age-related changes in the heart, lungs, and overall health more effectively than leading DNA-based "epigenetic clocks."

Pathology has long been the cornerstone of cancer diagnosis and treatment. A pathologist carefully examines an ultrathin slice of human tissue under a microscope for clues that indicate the presence, type, and stage of cancer.

To a human expert, looking at a swirly pink tissue sample studded with purple cells is akin to grading an exam without a name on it - the slide reveals essential information about the disease without providing other details about the patient.

Researchers at the University of Pennsylvania have launched Observer, the first multimodal medical dataset to capture anonymized, real-time interactions between patients and clinicians. Much like the medical drama The Pitt, which portrays life in the emergency room, Observer lets outsiders peer inside primary care clinics - only, in this case, none of the filmed interactions are fictional.

Lung infections like pneumonia are among the world’s top killers - but diagnosing them is notoriously hard.

Now, researchers at UC San Francisco have found a way to identify these infections in critically ill patients by pairing a generative AI analysis of medical records with a biomarker of lower respiratory infections.

Scientists at the Icahn School of Medicine at Mount Sinai have developed a novel artificial intelligence (AI) tool that not only identifies disease-causing genetic mutations but also predicts the type of disease those mutations may trigger.

The method, called V2P (Variant to Phenotype), is designed to accelerate genetic diagnostics and aid in the discovery of new treatments for complex and rare diseases.

Reduced coronary blood flow, measured with an artificial intelligence-based imaging tool, predicted future cardiovascular events in patients with suspected stable coronary artery disease.(1) These findings were presented at EACVI 2025, the flagship congress of the European Association of Cardiovascular Imaging (EACVI), a branch of the European Society of Cardiology (ESC).

While targeted radiation can be an effective treatment for brain tumours, subsequent potential necrosis of the treated areas can be hard to distinguish from the tumours on a standard MRI. A new study led by a York University professor in the Lassonde School of Engineering found that a novel AI-based method is better able to distinguish between the two types of lesions on advanced MRI than the human eye alone, a discovery that could help clinicians more accurately identify and treat the issues.