Closing Risk Stratification Gaps in Localized Prostate Cancer With AI
Dr. Daniel Spratt discusses the limitations of traditional prostate cancer risk stratification tools and the potential for AI to provide more accurate, individualized prognoses. By analyzing digital pathology slides, multimodal AI can offer continuous risk estimates that help clinicians decide on treatment intensity.
Why it matters
Integrating AI into oncology could significantly improve patient outcomes by reducing the uncertainty associated with current diagnostic methods.
Welcome back to another Urology Times Special Report. In this opening segment, Daniel Spratt, MD, chair and professor of radiation oncology at University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, and a member of the Case Comprehensive Cancer Center, discusses gaps in current risk stratification for localized prostate cancer and the growing role of artificial intelligence (AI) in addressing them.
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