US EditionHallmarks of artificial intelligence contributions to precision oncology
13 Articles
13 Articles
Hallmarks of artificial intelligence contributions to precision oncology
The integration of artificial intelligence (AI) into oncology promises to revolutionize cancer care. In this Review, we discuss ten AI hallmarks in precision oncology, organized into three groups: (1) cancer prevention and diagnosis, encompassing cancer screening, detection and profiling; (2) optimizing current treatments, including patient outcome prediction, treatment planning and monitoring, clinical trial design and matching, and developing …
Will AI ever cure cancer? The multibillion-dollar race to bring the first AI-discovered drug to market
For three weeks last May, employees of the AI giant Nvidia and Recursion Pharmaceuticals slept on the floor of a data center in Salt Lake City. They were there to build a machine that Recursion, a decade-old biotech company, believes will give it an edge in the contest to develop the next great new medicines: BioHive-2, the largest and fastest supercomputer ever to be used in the biopharmaceutical industry. It's an audacious bet that the future …
Innovative AI tool helps scientists better understand cancer
The AI tool allows scientists to see whether or not there may be cells becoming active in trying to combat cancer. By Crystal Jones, TPS Scientists from Tel Aviv University have written an AI algorithm, enabling them to better understand how genetic diseases like cancer develop, and respond to medication. The innovative tool, named “scNET” combines a vast amount of data collected from a human bodily sample to give scientists insight into how cel…
Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review - BMC Medical Research Methodology
Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that fully exploit the longitudinal data stored within electronic health records (EHRs). This review aims to summarise methods currently utilised for prediction of cancer from longitudinal data and provides recommendati…
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