Illustration of Revolutionizing Prostate Cancer Detection: AI Outperforms Doctors

Revolutionizing Prostate Cancer Detection: AI Outperforms Doctors

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An AI healthcare company claims its software can more accurately detect the extent of prostate cancer than doctors.

Avenda Health released a study last month involving ten doctors who each assessed 50 different prostate cancer cases. Avenda’s Unfold AI software detected cancer with 84.7% accuracy, while physicians manually detecting cancer had accuracies ranging from 67.2% to 75.9%.

Conducted in partnership with UCLA Health and published in the Journal of Urology, the study also found that using AI to assist with cancer contouring made predictions of cancer size 45 times more accurate and consistent than without AI.

“We observed that AI assistance made doctors both more accurate and more consistent, meaning doctors tended to agree more when using AI,” said Shyam Natarajan, an assistant adjunct professor of urology, surgery, and bioengineering at UCLA and senior author of the study, in a statement.

Doctors typically use MRIs to understand the size of a tumor, but some tumors are “MRI-invisible,” explained Dr. Wayne Brisbane, an assistant professor of urology at the David Geffen School of Medicine at UCLA. AI proves beneficial where MRIs fall short.

“Overall, the use of AI in cancer treatment could lead to more effective and personalized care for patients, with treatments better tailored to their individual needs and more successful in combating the disease,” Brisbane said. He added that AI can “go beyond human ability.”

Avenda Health CEO Dr. Shyam Natarajan remarked that it is “empowering for physicians to see this kind of innovation validated through studies and recognized by the AMA.”

According to the American Cancer Society, approximately 1 in 8 men in the US will be diagnosed with prostate cancer in their lifetime, and 1 in 44 will die from the disease.

It is estimated there will be 299,010 new cases of prostate cancer in the US this year, with 35,250 expected to die from the disease.

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