Beyond sensitivity: artificial intelligence and micro‐ultrasonography in prostate cancer detection

We congratulate Zhou et al. [1] for their important study, in which the Prostate Micro-Ultrasound Network (ProMUS-NET), a U-Net-based model, achieved higher sensitivity than expert urologists on micro-ultrasonography (MUS). This represents a step toward democratising targeted biopsy in contexts where multiparametric MRI (mpMRI) remains costly or inaccessible. However, beyond demonstrating technical performance, this study prompts a re-examination of artificial intelligence's (AI's) role in prostate cancer diagnostics. We highlight three perspectives. The ProMUS-NET underperformed in detecting anterior cancers, a limitation long recognised for MUS [1, 2]. Nearly one-third of prostate cancers arise anteriorly. This invites the insight that AI should not only compensate for perceptual errors but also stimulate advances in probe design, adaptive beamforming, or physics-informed algorithms. Although sensitivity improved, false positives increased [1]. Similar trade-offs have been observed in MRI-targeted pathways [3]. Translation into practice therefore requires balancing diagnostic yield with feasibility and patient anxiety. Interfaces presenting probabilistic outputs, rather than binary calls, may enable real-time adjudication by urologists. Inter-observer inconsistency is a major limitation of MUS [1]. AI re-frames this variability: the goal may be harmonisation rather than elimination. Hybrid frameworks that combine AI segmentation with clinician judgement and MRI priors could yield systems more robust than any single modality [4]. In conclusion, Zhou et al. [1] show that AI can elevate MUS beyond current human performance. Its greater promise may lie in catalysing hardware redesign, multimodal integration, and equitable deployment. This positions AI not as a competitor but as a catalyst for sustainable, clinician-led diagnostic ecosystems. No specific funding was received for this study. The authors declare no conflicts of interest.