With the help of artificial intelligence, computers are to simulate human thought processes. Machine learning is intended to support almost all medical specialties. But what is going on inside an AI algorithm, what are its decisions based on? Can you even entrust a medical diagnosis to a machine? Clarifying these questions remains a central aspect of AI research and development.
In a CT scan of the lungs, accidentally inhaled objects can be extremely subtle and easy to miss, even for experienced clinicians. A new AI model acts as a “second set of eyes” to help detect hidden cases.
Europe’s healthcare systems face growing pressure: fewer staff, higher infection-control demands, and increased documentation. Estonia is already built for this. With 99% of health data digital, digital trust is built into everyday healthcare – giving Estonian companies a strong foundation for efficient collaboration, fast traceability, and AI-supported operations.
Researchers developed an AI-assisted brain atlas for visualization at microscopic detail using MRI scans. The tool could transform early detection of Alzheimer's and other neurological diseases.
A biosensor that identifies volatile organic compounds (VOCs) in exhaled breath could be used in conjunction with AI to detect various thoracic cancers including lung cancer.
In emergency settings, AI to analyze ECG data can improve detection of severe heart attacks, including those with unconventional symptoms, or atypical ECG patterns, and reduced false positives.
Developmental dysplasia of the hip occurs in 2-4% of infants, but screening via ultrasound is notoriously hard to master. An AI-assisted application could prevent many unnecessary hospital referrals.
At the 38th European Association of Nuclear Medicine (EANM) Congress, held from October 4–8 in Barcelona, United Imaging, a global manufacturer of cutting-edge medical imaging technology, showcases its most advanced AI-powered molecular imaging innovations, the uMI Panvivo family and the uMI Panorama family, and highlights the company’s strong growth and confidence in the European market.
An international team has developed an AI model that brings together medical imaging data and clinical information to calculate the risk of breast cancer tumour recurrence more accurately.
Underscoring the growing role of large language models (LLM) in cancer care, ESMO released the first structured set of recommendations to bring AI language models into oncology safely and effectively.
An AI system that can predict what a patient’s knee X-ray will look like a year in the future could transform how millions of people with osteoarthritis understand and manage their condition.
Overcrowded EDs and the escalating workload of nurses are pressing challenges in emergency medicine. While AI might not solve these problems, it could help staff mitigate them, new research suggests.
Healthcare stands at a crossroads. With an impending shortage of 11 million healthcare workers by 2030 and millions dying annually from poor-quality care, the industry desperately needs transformation. Dr. Alex Ng from Tencent Healthcare explores how artificial intelligence is emerging as a powerful catalyst—not to replace human expertise, but to amplify it.
This year, the Journées Francophones de Radiologie (JFR) will carry a clinical ambition as simple as it is essential: to shine a spotlight on those who are often overlooked. Under the presidency of Professor Mathieu Lederlin, thoracic radiologist at Rennes University Hospital, vulnerable patients will be at the heart of the annual meeting of the French Society of Radiology that will unfold…
Using AI to help detect one of the leading killers of women worldwide: A new machine learning model can successfully predict heart disease risk in women by analysing mammograms.
Misinterpreting the malignancy risk of lung nodules often results in high false-positive rates, unnecessary follow-ups, increased patient anxiety and healthcare costs. A new study suggests that AI can fix this.
Digital transformation, including AI, is key to improving healthcare services - but this may be easier said than done: A new UK study identifies hurdles for AI implementation in NHS hospitals.
To tackle the shortage of donor organs, researchers have developed a 3D printer that doesn’t just print implantable tissues, it also sees and even co-designs parts of organs.
Use of an AI voice agent to prompt self-reported blood pressure readings may help improve accuracy of at-home blood pressure measures and patient outcomes in older patients with hypertension.
AI may be effective at detecting advanced breast cancer in mammograms, but current models still lack reliability. New research from Korea suggests that up to 14% of invasive cases are missed.
