AI vs. Doctors: Revolutionizing Prostate Cancer Detection

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An artificial intelligence healthcare company claims its software can more accurately identify the extent of prostate cancer than medical professionals.

Avenda Health released findings last month showing that ten doctors evaluated 50 distinct prostate cancer cases each. The company’s Unfold AI software achieved an accuracy of 84.7% in detecting cancer, compared to the accuracy of doctors, which ranged from 67.2% to 75.9%.

Conducted in collaboration with UCLA Health and published in the Journal of Urology, the study also highlighted that AI-assisted cancer contouring led to predictions of tumor size that were 45 times more accurate and consistent than traditional methods.

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

Typically, doctors rely on MRIs to gauge tumor size. However, some tumors are not visible on MRIs, as explained by Dr. Wayne Brisbane, an assistant professor of urology at the David Geffen School of Medicine at UCLA. He noted that AI technology can identify information that MRIs may overlook.

Brisbane expressed that the integration of AI in cancer treatment could enhance personalized care, resulting in therapies that are more effectively tailored to the patient’s unique needs and more successful in combating the disease. He emphasized that AI has the potential to “go beyond human ability.”

Dr. Shyam Natarajan, CEO of Avenda Health, remarked on the significance of this innovation being validated through studies and acknowledged by the American Medical Association.

According to the American Cancer Society, approximately 1 in 8 men will be diagnosed with prostate cancer in their lifetime, with 1 in 44 men succumbing to the disease. This year, an estimated 299,010 new prostate cancer cases are expected in the U.S., with 35,250 deaths attributed to the illness.

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