A multitude of recent studies and success stories suggest artificial intelligence is on its way to topping doctors in accurately diagnosing diseases from asthma to breast cancer—seemingly a step in the right direction. But does the hype surrounding AI’s victories eclipse its shortcomings?

Artificial intelligence shows great promise in recognizing patterns, liberating physicians from keyboards and predicting outcomes, but where does it fall short? Medical guru Eric Topol does a deep dive into those topics and many more in his new book, Deep Medicine.

A deep neural network crafted by research specialists at Dartmouth’s Norris Cotton Cancer Center identified different types of lung adenocarcinoma as well as practicing pathologists in a recent study, according to work published March 4 in Scientific Reports.

A 14-week health technology “sprint” sponsored by the U.S. Census Bureau and coordinated by HHS has produced an AI tool that developers claim could revolutionize the way researchers match cancer patients with clinical trials.

Canadian researchers working with Toronto General Hospital-University Health Network have developed a natural language processing (NLP) approach to predicting downstream radiology resource utilization, according to work published in the Journal of the American College of Radiology March 2.

The FDA on March 7 granted Breakthrough Device designation to Paige.AI, a year-old New York startup that’s using AI to help diagnose and treat cancer, the company announced.

The rapid advancement of AI technologies has left the medical community in a state of flux, unsure of where to direct their efforts to deliver the best, most effective care. But to one dean the answer is simple: prioritize patients.

Deep learning is an important element of AI that’s helping advance diagnostics and treatment, but it also remains relatively uncharted territory.