AI Outperforms Doctors in Prostate Cancer Assessment: A Game Changer?

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An AI healthcare firm claims its software can more accurately assess the extent of prostate cancer than medical professionals.

Avenda Health conducted a study last month involving ten doctors who evaluated 50 prostate cancer cases each. The company’s Unfold AI software achieved an accuracy rate of 84.7%, compared to the accuracy of the participating physicians, which ranged from 67.2% to 75.9%.

This research, conducted in collaboration with UCLA Health and published in the Journal of Urology, also indicated that when AI was used to assist in cancer contouring, predictions regarding tumor size were 45 times more accurate and consistent than those made without AI assistance.

Shyam Natarajan, assistant adjunct professor of urology, surgery, and bioengineering at UCLA and the study’s senior author, remarked that the introduction of AI assistance improved both the accuracy and consistency of doctors’ assessments, leading to greater agreement among them when AI was utilized.

Dr. Wayne Brisbane, an assistant professor of urology at UCLA’s David Geffen School of Medicine, explained that while doctors typically use MRIs to assess tumor sizes, some tumors are not visible on these scans. AI technology proves beneficial in such cases.

Dr. Brisbane emphasized that the integration of AI in cancer treatment could enhance personalized patient care, allowing for more tailored and effective treatments aimed at combatting the disease. He noted that AI has the potential to surpass human analytical capabilities.

Avenda Health CEO Dr. Shyam Natarajan expressed pride in the fact that such innovations are being validated through research and acknowledged by the American Medical Association.

According to the American Cancer Society, about 1 in 8 men in the U.S. will be diagnosed with prostate cancer in their lifetime, and 1 in 44 will die from it. This year, it is estimated that there will be 299,010 new cases of prostate cancer in the U.S., with 35,250 fatalities attributed to the disease.

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