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.
Using deep learning, researchers created AI-driven computer models to analyze corneal and retinal images to help eye doctors in the future.
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.
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.
An artificial intelligence (AI) model combining four methods of machine learning (ML) to accurately detect thyroid cancer from routine ultrasound image data has been developed by US researchers.
A new AI platform can analyze genomic data extremely quickly, picking out key patterns to classify different types of colorectal tumors and improve the drug discovery process.
Colonoscopies performed with AI support may yield an increase in the overall rate of detection of adenoma, or cancerous and precancerous polyps, by 27% in average-risk patients, according to new data.
Clear ethical standards and guidance are needed for AI in health settings to protect the relationship of trust between doctors and patients and to safeguard human rights, according to a new report.
Researchers from Boston University School of Medicine have developed a novel artificial intelligence algorithm to assess digital pathology data.
A combination of digital pathology and quantitative biomarker analysis in the emerging concept of ‘smart’ cytology has a potential role in the detection and diagnosis of cancer.
Artificial intelligence (AI) could help guide the post-treatment surveillance of non-small cell lung cancer (NSCLC) patients and improve outcomes as a result, according to a new study.
An algorithm built to assess scar patterns in patient heart tissue can predict potentially life-threatening arrhythmias more accurately than doctors can.
Big data is transforming diagnosis and medical care, but the critical challenge remains over how to equally apply the benefits it delivers across real-world clinical settings, according to industry expert Prof Benoît Macq.
A major new study shows that artificial intelligence (AI) is a promising tool for breast cancer detection in screening mammography programs.
Scientists have utilized the integration of lightweight material engineering and artificial intelligence to create an exoskeleton robot that can aid those with mobility impairments.
The AI-Guided Ultrasound Intervention Device is a lifesaving technology that helps a range of users deliver complex medical interventions at the point of injury.
A software based on deep learning with convolutional neural networks can accurately and consistently classify breast density on mammograms according to BI-RADS criteria.
A new type of AI companion is being designed to aid memory recollection, boost confidence and combat depression in people living with Alzheimer’s disease and other types of dementia.
Chromatograms from mass spectrometry feature characteristic peaks to signify the response of a target molecule. Artificial intelligence can help in the challenging task of identifying these peaks.
By employing artificial intelligence (AI) and robotics to formulate therapeutic proteins, researchers promoted tissue regeneration.
