The study, led by investigators from the Mass General Cancer Center, part of Boston-based Mass General Brigham, in collaboration with researchers at the Massachusetts Institute of Technology, created and tested Sybil, a machine learning tool designed to use a single low-dose chest scan to predict the risk of lung cancers occurring one to six years after a screening.
Sybil was able to accurately predict both short-term and long-term lung cancer risk, according to a Jan. 12 press release MIT shared with Becker’s. The study was published in the Journal of Clinical Oncology.
The researchers said this AI model aims to bring the research community one step closer to outgrowing legacy systems in the healthcare industry and help better treat current and future patients.
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.