Not so fast with explainable AI in healthcare, warns an international and multidisciplinary team of academics.

Various iterations of AI “hold tremendous promise” to help personalize counseling for individuals grieving the loss of a loved one. 

As machine learning progresses from research settings to clinical practice, how are clinicians to know they can trust the machine’s conclusions to guide care for actual patients?

Emergency physicians have a tough time identifying patients who have Crohn’s disease and truly need a CT scan to pinpoint the cause of acute abdominal distress.

At one of the largest medical schools in the U.S., less than a third of the students and only half the faculty are up to speed on healthcare-specific AI.

Infants in pain can’t describe the severity of their discomfort, but NICU nurses can e-learn how to gauge pain degrees according to standardized scales, allowing for prompt and appropriate pain-relief interventions.

Healthcare professionals looking to raise their AI game have a new chance to do so while earning CME credits as well as certification.

Many cases will be handled by primary-care providers, eye technicians and even patients themselves connected by telehealth and armed with commercial test kits and AI.

The deep learning models had nothing more to go on than routine abdominal CT scans acquired preoperatively.

Along with AI in its various iterations, the list may include virtual and augmented reality, 3D printing, robotics and other technologies currently changing healthcare delivery.