We are using intelligent machines for everything from self-driving cars to online searches. But how about leveraging artificial intelligence, or AI, to save lives? “A momentous change in health care is under way,” says Suchi Saria, an associate professor in computational biology at Johns Hopkins, with startups harnessing the recent explosion of electronic health data to help doctors make critical decisions and extend care to patients between appointments. Here are five ways that machine learning is poised to bring new rigor to medicine.
Developing pharmaceuticals can take decades. Silicon Valley’s Atomwise speeds things up with supercomputers that root out therapies from a database of molecular structures. Meanwhile, Berg Health also mines data for clues about why some people survive diseases—insights that can inform new therapies.
Silicon Valley–based Enlitic is ingesting thousands of medical images, from x-rays to CT scans, to help radiologists spot things like tiny fractures and small tumors. Cardiogram, an Apple Watch app, uses an algorithm to detect when changes in a user’s heart rate may signal a serious health disorder.
The National Institutes of Health–funded AiCure app uses a smartphone’s webcam and AI to autonomously confirm that patients are adhering to their prescriptions: critical for people with serious ailments and participants in clinical trials.
San Francisco–based startup Sense.ly has a slew of customers, from the National Health Service to UC San Francisco, for its virtual nurse, Molly. The interface uses machine learning to support patients with chronic conditions between doctor’s visits.
Part of a larger effort to offer individuals targeted diagnoses and treatments, Toronto startup Deep Genomics identifies patterns in huge data sets of genetic information, looking for mutations and linkages to disease.