Interoperability is the big problem that seems to smack all new entries in the healthcare innovations area.

AI continues to wow healthcare watchers with sharp guidance on clinical decisionmaking, accurate aids to risk assessment and bankable workflow efficiencies. But healthcare was, is and always will be about “human-to-human relationships, trust and healing.”

Researchers in Texas and Taiwan have collaborated to develop a deep-learning tool that can precisely asses the risk of breast cancer—and with it the need for biopsy—in patients with lesions of questionable concern found in mammograms.

Diagnosing coronary artery disease, the most common type of heart disease in the U.S., can be improved by AI, according to a new, multicenter international study published in The Journal of Nuclear Medicine.

A healthcare AI startup striving to become the biggest provider of virtual medicine in India has gotten a boost in the form of investor dollars and friendly coverage in a prominent business journal.  

Researchers have developed a deep-learning framework that can show how mutations in “noncoding DNA”—meaning parts of the strand that contain no genes—contribute to autism. And they believe their algorithm is generalizable for clinical researchers studying the role of noncoding mutations in just about any disease.

The U.K. is taking on a big pilot program with 500,000 people being remotely monitored at home using AI to analyze all the incoming data. The program by the National Health Service underscores where AI is likely to have the biggest impact in healthcare––non-consumption, or areas where there isn’t an affordable or convenient solution for consumers.

A peer-reviewed journal has put out a call for papers to publish in an upcoming special issue on cutting-edge uses of AI in early-phase drug development.