Artificial Intelligence in the Multidisciplinary Tumor Board for Prostate Cancer: A Narrative Review

Multidisciplinary tumor boards (MDTs) guide prostate cancer decisions but are strained by rising case volumes, evolving guidelines, and growing complexity. Artificial intelligence (AI) now enters the MDT at two levels. First, it generates inputs: deep-learning magnetic resonance imaging (MRI) reading matches or exceeds radiologists’ performance in the Prostate Imaging–Cancer Artificial Intelligence (PI-CAI) study; Gleason grading reaches expert-level agreement; PSMA PET quantification is automated; and multimodal pathology biomarkers predict ADT benefit. Second, decision-support systems act on MDT output: rule-based tools reproduce standard recommendations in 86–99% of cases, mostly in populations dominated by standard cases, whereas general-purpose large language models (LLMs) agree with real urological boards in 56–93% of cases, depending largely on input quality and case mix, and perform worse in metastatic disease; agreement does not establish clinical benefit. Of 33 appraised decision-support studies, 27 were at high risk of bias, 20 of them solely because the treating board served as an unblinded reference standard; explainability, regulation, and automation bias remain unresolved. We propose that AI currently serves as a supervised pre-reader, guideline checker, and triage tool. Prospective multicenter studies with patient outcomes are needed.