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.
Cardisiography is a screening method for detecting structural, rhythmical, or vascular heart disease and a decision support tool to guide targeted follow-up. Meik Baumeister, CEO of Cardisio, talks about the development and the operating principle.
It might sound like science fiction but it is reality in cardiology: with the help of artificial intelligence (AI) physicians can recognize from a patient’s headshot whether the person is suffering from coronary artery disease and is therefore at risk of myocardial infarction. But is that knowledge really useful? Professor Dr David Duncker calls for a differentiated and careful assessment of…
UK researchers developed and validated a deep learning algorithm that can identify and outline a non-small cell lung cancer (NSCLC) tumor on a CT scan within seconds.
A device with the appearance of a Wi-Fi router uses a neural network to discern the presence and severity of one of the fastest-growing neurological diseases in the world: Parkinson's.
Striving to improve the patient’s quality of life after laryngectomy, researchers conducted a study, during which they searched for pathologies in patients’ voices using artificial intelligence (AI).
Researchers for the first time compared schizophrenia and frontotemporal dementia, disorders that are both located in the frontal and temporal lobe regions of the brain.
The portfolio Philips presented at ECR 2022 revealed that the company not only advanced their products, but also listened to medical professionals and patients – and took their feedback to heart.
Researchers developed an AI that accurately and quickly diagnoses idiopathic pulmonary fibrosis, based only on information from lung images and medical information collected during daily medical care.
As knowledge about Covid-19 advances, so does the arsenal of techniques to predict, diagnose and follow up on the disease. At this year’s ECR, researchers presented a range of promising imaging modalities to keep track of Covid-19 symptoms, severity, and mortality, often including AI support to enhance or accelerate diagnostics.
Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study shows.
Fujifilm partners with Gleamer to integrate an AI software called BoneView into its X-ray imaging systems to assist radiologists and emergency clinicians in the diagnosis of skeletal fractures.
AI-based diagnosis is undoubtedly one of the most promising subjects when we talk about the future of radiology. Now, a couple of new studies indicate that most radiologists are open to using the technology and this for good reasons.
Building artificial intelligence (AI) tools that clinicians and patients can trust, and easily use and understand, are core to the technology being successfully deployed in healthcare settings.
MIT researchers produced textiles that sense the wearer’s posture and motions. Their “smart” shoes and mats could be used in applications ranging from health care to prosthetics.
Physicians use AI-powered technology for faster and earlier detection of diseases. At ECR Overture, Dr Steven Schalekamp, PhD, discussed the application of AI for chest radiography in paediatrics.
Using deep learning, researchers created AI-driven computer models to analyze corneal and retinal images to help eye doctors in the future.
The classification of brain tumors—and thus the choice of optimal treatment options—can become more accurate and precise through the use of artificial intelligence in combination with physiological imaging.
Incorporating AI support into clinical practice can reduce repetitive tasks, saving approximately one hour of chest CT interpretation time in a radiologist’s typical workday.
Artificial intelligence can help diagnose acute heart failure with more accuracy than current blood tests alone, research suggests.
