Until recently, humans have relied on the trained eyes of doctors to diagnose diseases from medical images.
Beijing-based Infervision is among a handful of artificial intelligence startups around the world racing to improve medical imaging analysis through deep learning, the same technology that powers face recognition and autonomous driving.
The startup, which has to date raised $70 million from leading investors like Sequoia Capital China, began by picking out cancerous lung cells, a prevalent cause of death in China. At the Radiological Society of North America’s annual conference in Chicago this week, the three-year-old company announced extending its computer vision prowess to other chest-related conditions like cardiac calcification.
“By adding more scenarios under which our AI works, we are able to offer more help to doctors,” Chen Kuan, founder and chief executive officer of Infervision, told TechCrunch. While a doctor can spot dozens of diseases from one single image scan, AI needs to be taught how to identify multiple target objects in one go.
But Chen says machines already outstrip humans in other aspects. For one, they are much faster readers. It normally takes doctors 15 to 20 minutes to scrutinize one image, whereas Infervision’s AI can process the visuals and put together a report under 30 seconds.
AI also addresses the long-standing issue of misdiagnosis. Chinese clinical newspaper Medical Weekly reported that doctors with less than five years’ experience only got their answers right 44 percent of the time when diagnosing black lungs, a disease common among coal miners. A research from Zhejiang University that examined autopsies between 1950 to 2009 found that the total clinical misdiagnosis rate averaged 46 percent.