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Open Call DIGITAL-2026-AI-09-SOLUTIONS-CANCER-STEP: Deployment of Cutting-Edge Multi-Modal AI-Based Solutions in Medical Imaging

By building on, extending and leveraging the Cancer Image Europe platform, this action is expected to facilitate the uptake of EU AI-driven solutions in medical imaging (including Machine Learning and Generative AI), towards their deployment in clinical settings. It should also include upskilling of healthcare professionals and evidence generation to evaluate the performance of the deployed AI-driven solutions and engage patients.

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AI Detects Early Signs of Aging from Chest X-Rays

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."

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Researchers Discover Bias in AI Models that Analyze Pathology Samples

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.

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New Video Dataset to Advance AI for Health Care

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.

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A New Way to Diagnose Deadly Lung Infections and Save Lives

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.

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Philips Agrees to Acquire SpectraWAVE Inc.

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, today announced it has entered into an agreement to acquire SpectraWAVE, Inc., an innovator in Enhanced Vascular Imaging (EVI) of coronary arteries, angiography-based physiology assessments, and the use of AI in medical imaging [3]. SpectraWAVE’s intravascular imaging and physiological assessment technologies provide advanced solutions for the treatment of patients with coronary artery disease, the most frequent type of heart disease, affecting more than 300 million people worldwide [4].

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New AI Tool Identifies not Just Genetic Mutations, but the Diseases they may Cause

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.

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AI-Based Tool Predicts Future Cardiovascular Events in Patients with Angina

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).

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Novel AI Technique Able to Distinguish Between Progressive Brain Tumours and Radiation Necrosis

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.

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AI Supports Home-Based Balance Training

Balance training patients may soon be able to get AI feedback during home exercises, with four wearable sensors and a new machine learning model developed at the University of Michigan.

The team hopes their technology could help patients make faster progress during physical therapy and maintain their abilities after the end of their prescribed sessions.

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