AI Triumphs in Detecting Prostate Cancer: Are Doctors Losing Ground?

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A healthcare technology company claims its software can detect the extent of prostate cancer more effectively than human doctors.

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

This research, in collaboration with UCLA Health and published in the Journal of Urology, also showed that AI-assisted cancer contouring enhanced predictions of tumor size, making them 45 times more accurate and reliable compared to traditional methods.

Shyam Natarajan, an assistant adjunct professor of urology, surgery, and bioengineering at UCLA and one of the study’s senior authors, noted that AI support improved doctors’ accuracy and consistency, leading to a higher level of agreement among them.

While doctors typically rely on MRI scans to assess tumor size, some tumors are “MRI-invisible,” according to Dr. Wayne Brisbane, an assistant professor of urology at UCLA’s David Geffen School of Medicine. He emphasized that AI can provide valuable insights where MRI scans fall short.

Brisbane remarked that integrating AI in cancer treatment could result in more effective and personalized patient care, allowing for treatment plans that are better customized to meet individual needs and enhance treatment success. He added that AI can extend capabilities beyond human potential.

Avenda Health’s CEO, Dr. Shyam Natarajan, emphasized the significance of such innovations gaining validation through research and recognition from the American Medical Association (AMA).

In the United States, approximately 1 in 8 men will be diagnosed with prostate cancer at some point in their lives, and 1 in 44 men will succumb to the disease, as reported by the American Cancer Society. This year, it is estimated that there will be 299,010 new prostate cancer cases in the U.S., with 35,250 fatalities attributed to the illness.

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