Drug development is on the cusp of becoming considerably faster, smarter and all-around better.

Options are increasing for healthcare consumers looking to check their symptoms with an AI digital platform for self-diagnosis. However, research into the use, accuracy and regulation of these technologies is woefully scant.

Researchers are looking into whether live and automated text messaging augmented by AI can help treat or ward off postpartum depression for women in Kenya.

Healthcare technology moves at lightning speed, with AI and machine learning at the forefront of innovation. Right alongside these new discoveries is blockchain technology, which was popularized through the rise of cryptocurrency, and is seeing its own emergence in healthcare.

Using deep learning to tease out factors indicative of age-related variability in the way toddlers gaze at visual stimuli, researchers at the University of Minnesota have shown that the technology can accurately distinguish 18-month-olds from 30-month-olds.

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%.