AI is becoming more accessible to researchers of any age after The Penn Medicine Institute for Biomedical Informatics launched a free, open-source automated machine learning system for anyone to use, Penn AI.

Facebook researchers are working to see how well and how quickly robots can teach themselves to walk, feel their way around tricky spaces and otherwise try new robot-y things through AI-driven trial and error.

Deep learning can help diagnose skin cancer with high accuracy even when it has only low-tech dermoscopic images to work with, according to research conducted in Israel and published in The Lancet’s online journal eBioMedicine.

An AI algorithm created by Google can predict lung cancer with high accuracy and improve the survival chances of those with the cancer through earlier diagnosis, according to a recent study. The findings were published in Nature Medicine on May 20.

People who struggle to get a good night’s sleep and seek medical help for the problem are producing mega data on things like eye movement, breathing, brain activity and restless legs. Which is to say sleep medicine is as ripe as any field in healthcare for help from AI.

A Colorado teenager has won $75,000 for his machine learning and computer vision project that helps orthopedic surgeons improve the accuracy of screw placement during spinal surgery.

Contrary to common understanding, sepsis is not a one-size-afflicts-all condition. It’s a set of many, and each can be placed into one of four key subcategories—i.e., phenotypes—such that, going forward, stricken patients could receive tailored treatments.

A natural language processing algorithm has achieved 90% precision in automatically spotting signs of social isolation in cancer patients by “reading” clinical notes in a hospital’s electronic health record.

A San Jose-based company focused on machine learning for care optimization, Health[at]Scale, has raised $16 million in a Series A funding round, with Optum as its sole investor.

Academic and popular writings on the use of “embodied” AI in mental healthcare are piling up fast. But where’s the guidance for psychiatrists, psychotherapists and clinical social workers looking to use robots, avatars and chatbots with real patients?