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Basic, translational and clinical research improves, extends and saves lives.
Refining algorithms by incorporating more images of people of color can teach AI to better diagnose melanoma and other skin cancers.
Artificial intelligence and machine learning can help identify genetic changes that lead to cancer—and which treatments work best.
Using artificial intelligence software during a colonoscopy can reduce the number of overlooked polyps left behind in the colon.
Episode 24 with Sacheim Sharma, MD, MPH, and Gretchen Hubbard, PhD, Director, at Caris Life Sciences.
Individuals considered at high risk were 25 times more likely to develop pancreatic cancer within three years.
Clinical trials that test new cancer drugs or combinations seldom account for other medications a patient may be taking.
AI tools have the potential to make cancer imaging faster, more accurate and even more informative. And that’s generating excitement.
P-NET is a “biologically informed deep learning model” capable of guessing a prostate cancer patient’s disease state.
Images used to train artificial intelligence to diagnose skin cancer rarely include non-white people.
Highlights from the over 400 abstracts related to colorectal cancer presented at The American Society of Clinical Oncology’s annual meeting
A novel artificial intelligence device known as GI Genius could detect precancerous polyps that clinicians may otherwise overlook.
The GI Genius aids clinicians in detecting potential irregularities during colon cancer screening and surveillance.
An inside look at five research developments that have the potential to transform the treatment of cancer
Artificial intelligence may help predict response to immune checkpoint blockade in patients with metastatic melanoma
Combining machine learning with cell engineering, scientists can design ‘living medicines’ that precisely target tumors
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