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.
While generative AI shows immense potential for healthcare, a critical reliability issue lurks beneath the surface: LLMs don't think like doctors do, a data science expert explained at the Emerging Technologies in Medicine (ETIM) congress in Essen. This potentially fatal flaw, however, may be fixable, he suggested.
Despite previous global declines, pulmonary tuberculosis (TB) is on the rise again. A new AI-powered lung ultrasound shows promise in improving diagnostic performance of TB.
A new AI tool can extract key information from brain MRI scans of multiple sclerosis (MS) patients. This could be used to improve disease and treatment response monitoring.
A network of artificial neurons imitating the human brain could help identify patients at risk of a serious arrhythmia from an ECG and avoid many cases of sudden cardiac death, a new study suggests.
The evolving role of AI tools in digital pathology was explored at an open discussion during the annual Digital Pathology and AI Congress in London with a high-level panel of practitioners looking at current and future technology options. The panel of pathologists, scientists and academics from Europe and the USA assessed the tools they currently use and are available to them, and those they…
Contrast agents, AI and circular economy: Bayer presented new developments for radiology at the ECR Congress. Particular emphasis was placed on digital assistance systems and sustainability.
By using AI to analyse ECG data, researchers created an algorithm to predict the biological age of the heart. This could be used to identify those most at risk of cardiovascular events and mortality.
The location and timing of breast cancer recurrence may allow AI to predict the risk of metastasis, a new study shows. This is an essential step towards developing personalised treatment strategies.
Assistive AI systems are designed to ease the workload of physicians. However, implementation of the technology could actually worsen challenges related to error prevention and burnout, experts warn.
A greater emphasis on AI and sustainability, new approaches to mitigating staff shortage and more: At the European Congress of Radiology (ECR) 2025 in Vienna, Philips showcased its approaches to several critical issues in diagnostic imaging. The global healthtech company presented solutions addressing today's healthcare challenges while aligning with the congress's "Planet…
A rigorous review of evidence showed that AI-assisted technology helps identify colorectal polyps. However, its impact on preventing colon cancer remains unclear.
New findings show how AI-assisted mammography may not only reveal breast cancer, but can also assess calcium buildup in the arteries within breast tissue—an indicator of cardiovascular health.
For Esaote, a leading Italian innovator in medical imaging - ultrasound, dedicated magnetic resonance and medical IT - the mission is to develop new technologies that can help advance diagnosis and improve patient care. Esaote has always leveraged continuous innovation as the key driver of its business strategy. This approach is also embodied in its three business areas.
To reduce the radiation exposure for patients undergoing frequent CT scans for pneumonia diagnosis, deep learning-based denoising of ultra-low dose CT presents a viable alternative.
Machine learning models fail to detect key health deteriorations in the ICU: a new study reveals that 66% of critical injuries in hospitals would go unnoticed if the current AI models were put to use.
Generative AI can detect and quantify cognitive biases of human caregivers during medical emergency situations. This way, LLMs could help reduce the impact of bias based on gender, age, or ethnicity.
A new study reveals that AI can accurately rule out negative low-dose CT (LDCT) scans, potentially reducing the workload of radiologists in lung cancer screening by up to 79%.
A new AI-based approach can predict whether people with type 2 diabetes are likely to develop chronic kidney disease years before symptoms arise, allowing for earlier intervention and treatment.
Researchers developed an advanced AI tool for automatic analysis of colorectal cancer tissue slides. The new model outperformed all predecessors in the classification of tissue microscopy samples.
