Machine learning could play a significant role in the future treatment of schizophrenia patients, according to new research published in JAMA Network Open.

A new international partnership is focused on using AI technology to take a closer look at the human brain and advance amyotrophic lateral sclerosis (ALS) research.  

Close to 1 in 4 Americans would be willing to use a healthcare AI tool, app or technology as long as it met one criterion: lowering the cost of their care.

After poring over the chemical compositions of more than 107 million molecules used to make all sorts of drugs, a machine learning algorithm has plucked out one unexpected candidate that may be medicine’s best hope yet against dreaded superbugs.

When it comes to getting paid patients’ portions for online medical consultations, physicians do better to engage patients attentively and provide high-quality service during the virtual visits than to rely on their standing reputations.

Healthcare AI giant IBM Watson Health has introduced software that can help medical researchers design clinical studies right the first time so as to minimize the need for tweaking and fine-tuning while their study is underway.

Researchers have developed two AI-powered tools for automatically extracting key information from free-text pathology reports. The team shared its findings in the Journal of the American Medical Informatics Association.

Machine learning-based CT texture analysis can help with the evaluation of solid renal masses, according to new findings published in Academic Radiology. Could this help reduce the number of patients undergoing unnecessary surgeries?

AI technology could replace countless jobs in the not-so-distant future, making an impact on workforces all over the world. According to a new analysis published in Information and Organization, researchers and policymakers alike should pay especially close attention to this development and get involved now—before it’s too late.

Numerous deep learning models can detect and classify imaging findings with a performance that rivals human radiologists. However, according to a new study published in the Journal of the American College of Radiology, many of these AI models aren’t nearly as impressive when applied to external data sets.