AI vs. Doctors: Revolutionizing Prostate Cancer Detection

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A healthcare technology company, Avenda Health, claims that its AI software can identify the extent of prostate cancer more accurately than human doctors.

In a recent study involving ten physicians who each evaluated 50 prostate cancer cases, Avenda’s Unfold AI software achieved a cancer detection accuracy of 84.7%. In contrast, the doctors’ manual assessments varied, yielding accuracies between 67.2% and 75.9%.

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

Shyam Natarajan, an assistant adjunct professor of urology, surgery, and bioengineering at UCLA and the study’s senior author, noted that the integration of AI technology made physicians’ diagnoses both more reliable and uniform, resulting in greater consensus among doctors.

Typically, doctors utilize MRIs to gauge tumor sizes; however, Dr. Wayne Brisbane, an assistant professor of urology at the David Geffen School of Medicine at UCLA, pointed out that some tumors evade detection on MRIs, indicating a crucial role for AI.

Brisbane emphasized that the application of AI in cancer treatment could enhance patient care by delivering personalized therapies tailored to individual needs and improving treatment outcomes. He remarked that AI has capabilities that surpass human experts.

Dr. Shyam Natarajan, CEO of Avenda Health, expressed optimism about advancements in medical technology being validated through research and gaining acknowledgment from the American Medical Association.

According to the American Cancer Society, approximately one in eight men in the United States will be diagnosed with prostate cancer, and one in 44 men will succumb to the disease. This year alone, estimates predict that there will be 299,010 new cases of prostate cancer with 35,250 fatalities resulting from the illness.

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