A group of investigators led by Cedars-Sinai have developed and successfully tested a new artificial intelligence (AI) method to make launching cancer clinical trials easier and faster. The method uses patients' pathology reports to automate the classification of patients by the severity of their cancers, potentially shortening the process of selecting candidates for clinical trials. Their achievement, described in Nature Communications , significantly expands AI's health care applications.
The new AI method, also called a model, offers a much-needed alternative to tumor registries, the databases maintained by governments and hospitals. Researchers normally use tumor registries to screen cancer patients for clinical trials . Cancer registries require specially trained employees to manually identify a patient's cancer stage by reviewing laboratory reports, clinicians' notes and other information.
The process can be slow and tedious. "By the time a cancer patient's data is entered into a tumor registry, months may have passed, along with the opportunity for the patient to participate in relevant clinical trials or other treatments," said Nicholas Tatonetti, Ph.D.
, vice chair of Computational Biomedicine at Cedars-Sinai, associate director for Computational Oncology at Cedars-Sinai Cancer and corresponding author of the study. "Our AI model can dramatically reduce that delay, accelerating the pace of research and expanding patients' access to clinical trials." The team's AI mode.
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