In the study, a system using machine learning and natural language processing analyzed nearly 9,000 narrative CT radiology reports. The technology was able to detect and triage high-risk pulmonary nodules in the text of the reports with over 90 percent precision.
“This approach promises to improve healthcare quality by increasing the rate of appropriate lung nodule incidental finding follow-up and treatment without excessive labor or risking overutilization,” the study’s authors wrote.
More articles about AI:
Optum funnels $16M into AI startup Health[at]Scale
IBM’s AI distinguishes between healthy, cancerous cells in breast tumors
UPenn Medicine’s AI tool for data analytics is open-source, free to the public
At the Becker's 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health, taking place September 14–17 in Chicago, healthcare executives and digital leaders from across the country will come together to explore how AI, interoperability, cybersecurity, and revenue cycle innovation are transforming care delivery, strengthening financial performance, and driving the next era of digital health. Apply for complimentary registration now.