Research

Months-old health technology startup Theator closed a $3 million seed round April 18, providing the company with the cash flow it needs to power its AI-enabled surgical performance platform.

The Department of Energy (DOE) announced April 17 it would be extending $20 million in funding to research projects involving AI and machine learning.

A Harvard Medical school research fellow has used deep learning to predict the structure of any given protein based solely on its amino acid sequence.

Researchers from the National Institutes of Health, Radiological Society of North America, American College of Radiology and Academy for Radiology and Biomedical Imaging Research have published what they’re calling a “roadmap for AI” in medical imaging—a framework for accelerating foundational research in the field.

As precision medicine transforms disease treatment into a patient-by-patient art and science, AI is poised to help quickly identify or even predict genetic mutations, pointing the way to highly targeted therapies.

The majority of recent journal studies evaluating the performance of AI algorithms failed to adequately validate test results, according to a meta-analysis published in the Korean Journal of Radiology, meaning most of that research can only serve as proof-of-concept and might not translate into clinical performance.

Researchers at North Carolina State University have developed a model that cuts the training time for deep learning networks by up to 69%.

AI and its various applications are redefining the way scientists approach cancer research, according to a review published in Drug Discovery Today.

Facial analysis enabled by AI is helping researchers assess mental health in a more objective way, relying on algorithms that detect behavioral biomarkers rather than subjective exams alone, the Wall Street Journal reported April 1.

Eye exams that leverage machine learning to track eye movement behavior are providing universities, research institutions and governmental agencies with more than a billion data points to better understand the connection between vision and neurological disease, according to health tech company RightEye.

Combing social networks and pharmaceutical databases using heterogeneous network mining allowed a team from Drexel University to better identify therapies suitable for drug repositioning, according to work published in Artificial Intelligence in Medicine.

Researchers at Ghent University in Belgium have tapped AI to help track the development of organisms at the level of the individual cell.