Critical appraisal of artificial intelligence studies for prostate cancer detection on MRI.

The application of artificial intelligence (AI) to prostate MRI for clinically significant prostate cancer (csPCa) detection is an active area of medical imaging research. Yet, methodological deficiencies can undermine the reliability and clinical translatability of many published studies. This perspective provides a structured critical appraisal of AI studies in prostate MRI, organised around six evaluation domains: (i) limits of predictability, (ii) reference standard quality, (iii) cohort design and population selection (including spectrum and pathway biases), (iv) benchmark and comparator validity, (v) clinical deployment, and (vi) AI terminology. We illustrate each domain using recently published studies as representative examples, and we ground our perspective in the formal requirements recently established by the PI-RADS Steering Committee for AI development and reporting in biopsy-naive men. Our analysis suggests that apparent near-perfect performance can arise from identifiable and preventable study design choices rather than from technical advancements. Methodological shortcomings may be mistaken for genuine advances; ensuring that the evidence base meets these standards is a prerequisite for population-general utility. KEY POINTS: Question Can the reported near-perfect AI performance for prostate cancer detection on MRI be attributed to study design choices instead of technical advances? Findings Apparent near-perfect diagnostic accuracy can arise from identifiable study design choices, including spectrum bias, pathway bias, imperfect reference standards, and confounded comparisons. Clinical relevance Adopting structured appraisal criteria aligned with PI-RADS Committee requirements helps clinicians distinguish methodologically sound AI tools from those whose reported performance may not generalise to routine clinical populations.