Analyzing the scientific literature on medical AI published over the past 46 years, researchers in the U.K. have found the U.S. far ahead of the field for sheer quantity.
Google Health all but invited the blowback when its AI developer-researchers suggested their breast-cancer model may be superior to radiologists’ eyes and generalizable across differing demographics.
If big data is to fulfill its potential for advancing the state of modern healthcare, developers of medical AI must be willing to show their work.
Three machine learning algorithms have identified patients likely to suffer extreme pain following surgery with about 80% accuracy each.
Researchers at Columbia University have developed a machine learning algorithm that identifies and predicts gender-based differences in adverse reactions to drugs.
A small but mighty research outfit based in Hungary has compiled a user-friendly database of medical technologies anchored in AI and approved by the FDA.
A multinational group of scholars has put together two fresh sets of guidelines for researchers testing AI applications in clinical trials.
Researchers in Canada are working to develop AI models for diagnosing and treating mental illness. One application in their sights involves automated interpretations of brain scans.
When a virus mutates, the researchers explained, it can be benign or even make the virus less dangerous to humans. In this instance, however, many detected mutations have a significant chance of becoming more infectious strains of COVID-19.
Computerized clinical decision support has strong upsides and few to no downsides for both clinicians and patients, according to a systematic literature review.
Concepts from the art and science of deep learning for medical practice can inform those employed in the art and science of medical education.
Medical journals accepting reprint fees are much more likely to publish articles written by authors who received industry payments.