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.
Machine learning (ML) for personalised care, large language models for empathy training of cardiologists, wearable sensor data for better screening, and more: Digital technologies hold great potential to improve diagnosis and treatment of patients with atrial fibrillation (AF). At the ESC 2024 cardiology congress in London, four experts explored the benefits of new solutions and pointed out…
If data used to train AI models for medical applications differs from the real-world data, it could lead to patient harm. A new study found proactive strategies to mitigate such data shifts.
Ground-breaking innovation for endovascular surgery: The new software package Endovascular Navigation supports physicians in their interventions with planning, navigation and fusion imaging.
United Imaging, a global leader in cutting-edge diagnostic imaging and radiotherapy solutions, proudly unveils two groundbreaking additions to its portfolio — the uMI Panvivo and the uMR Ultra — both newly CE-marked and set to redefine the standards of precision and performance in medical imaging.
A major challenge in cancer genomics is separating meaningful mutations from false positives. A new tool uses machine learning to significantly reduce these errors.
Cataracts are often treated via SICS surgery in low- and middle-income countries, which often leads to poorer results due to limited resources and training. A new AI-based tool aims to improve this.
Survival rates for pancreatic cancer rise drastically the earlier it is detected, but early-stage tumors are notoriously difficult to spot. A new AI-powered diagnostic system is set to improve this.
A deep learning model was able to predict future lung cancer risk from a single low-dose chest CT (LDCT) scan, according to new research published at the ATS 2025 International Conference.
Movement disorders often show overlapping symptoms, making it difficult for doctors to make the correct diagnosis. A new AI tool could help distinguish between different disorders, such as tremor and myoclonus.
As Emergency Departments (EDs) get ever busier, focus has fallen on the role artificial Intelligence (AI) can play in supporting patients and clinicians in delivering urgent care. The topic took centre stage in a session looking at the ethics of AI in the ED at the annual congress of the European Society of Emergency Medicine (EUSEM) in Copenhagen.
AI has the power to transform healthcare, but trust gaps among clinicians and patients threaten to slow adoption and impact, the 10th annual Future Health Index (FHI) report reveals.
Researchers have found a way to observe clotting activity in blood as it happens — without needing invasive procedures. This could open the door to better treatment of coronary artery disease (CAD).
Integration of large language models (LLMs) in healthcare is still in its early stages, but expected to expand rapidly. Now, researchers point out potential cybersecurity vulnerabilities in radiology.
Reading an LLM-generated recap to get the gist of a scientific publication? Not a good idea, a new study finds: Most leading chatbots exaggerate the findings – prompting for more accuracy even makes things worse.
Researchers evaluated the clinical value of using ChatGPT to interpret seizure semiology in order to predict which brain areas to remove to achieve seizure freedom in epilepsy patients.
A new AI tool called PERISCOPE should help healthcare providers determine which patients need extra monitoring to protect them from wound or lung infections, UTIs or sepsis.
A novel deep-learning approach automatically finds and classifies microcalcifications found in mammography images—bringing both accuracy and consistency to breast-cancer screening.
Results of a new retrospective study demonstrate the potential of a novel, CT-based deep learning-driven tool to enhance liver cancer diagnosis, treatment planning, and response evaluation.
Using AI to analyse ultrasound scans can detect up to 35% more risk pregnancies than scans performed by healthcare professionals without AI decision support, according to new results.
