AI lately has achieved dazzling success interpreting X-rays and other medical imagery in the lab. Now it’s catching on in the clinic.
What’s new: Roughly one-third of U.S. radiologists use AI in some form in their work, according to a survey by the American College of Radiology. One caveat: Many who responded positively may use older — and questionable — computer-aided detection, a technique for diagnosing breast cancer that dates to the 1980s, rather than newer methods.
What they found: The organization queried its membership via email and received 1,861 responses.
- Of respondents who said they use AI, just over half use it to interpret images, and another 11 percent for image enhancement. The most common applications were breast (45.7 percent), thoracic (36.2 percent), and neurological (30.1 percent) imaging.
- 12 percent of AI users said they use the technology to manage work lists, 11 percent to manage operations.
- Nearly 10 percent of AI users built their own algorithms rather than buying from outside vendors.
- 94 percent of AI users said their systems perform inconsistently. Around 6 percent said they always work, and 2 percent said they never work.
- More than two thirds of respondents said they don’t use AI, and 80 percent of those said they see no benefit in it. Many believe that the technology is too expensive to implement, would hamper productivity, or wouldn’t be reimbursed.
Behind the news: AI’s role in medical imaging is still taking shape, as detailed by Stanford radiology professor Curtis Langlotz in the journal Radiology: Artificial Intelligence. In 2016, a prominent oncologist wrote in the New England Journal of Medicine, “machine learning will displace much of the work of radiologists.” Two years later, Harvard Business Review published a doctor-penned essay headlined, “AI Will Change Radiology, but It Won’t Replace Radiologists.” Radiology Business recently asked, “Will AI replace radiologists?” and concluded, “Yes. No. Maybe. It depends.”
Why it matters: AI’s recent progress in medical imaging is impressive. Although the reported 30 percent penetration rate probably includes approaches that have been uses for decades, radiologists are on their way to realizing the technology’s promise.
We’re thinking: One-third down, two-thirds to go! Machine learning engineers can use such findings to understand what radiologists need and develop better systems for them.