Introduction:PI-RADS 3 lesions represent a diagnostic grey zone on multiparametric MRI, with clinically significant prostate cancer (csPCa) detected in only 10–30%. Their equivocal nature leads to both unnecessary biopsies and missed cancers. Artificial intelligence (AI) has emerged as a potential tool to provide objective, reproducible risk prediction. This review summarises current evidence on AI for risk stratification in patients with indeterminate mpMRI findings, including clarification of key multicentre initiatives such as the PI-CAI (Prostate Imaging–Artificial Intelligence) study—a global benchmarking effort comparing AI systems against expert radiologists.Methods:A narrative review of PubMed and Embase (search updated to August 2025) was conducted using terms including “PI-RADS 3”, “radiomics”, “machine learning”, “deep learning”, and “artificial intelligence.” Eligible studies included those evaluating AI-based prediction of csPCa in PI-RADS 3 lesions using biopsy or long-term follow-up as reference standards. Both single-centre and multicentre studies were included, with emphasis on externally validated models.Results:Radiomics studies demonstrate that handcrafted features extracted from T2-weighted and diffusion-weighted imaging can distinguish benign tissue from csPCa, particularly in the transition zone, with area-under-the-ROC curves typically 0.75–0.82. Deep learning approaches—including convolutional neural networks and large-scale representation-learning frameworks—achieve higher performance and can reduce benign biopsy rates by 30–40%. Models that integrate imaging-based AI with clinical predictors such as PSA density further improve discrimination. The PI-CAI study, the largest international benchmark to date (>10,000 MRI exams), shows that state-of-the-art AI systems can match or exceed expert radiologists for csPCa detection across diverse scanners, centres, and populations, though prospective validation remains limited.Conclusions:AI shows strong potential to refine management of PI-RADS 3 lesions by reducing unnecessary biopsies, improving csPCa detection, and mitigating inter-reader variability. Translation into routine practice will require prospective multicentre validation, harmonised imaging protocols, and integration of AI outputs into clinical workflows with clear thresholds, decision support, and safety-net recommendations.
引言：PI-RADS 3类病灶在多参数磁共振成像中属于诊断灰色区域，其中仅10%-30%被检出具有临床意义的前列腺癌。其不确定性既可能导致不必要的穿刺活检，也可能造成漏诊。人工智能作为一种潜在工具，能够提供客观、可重复的风险预测。本综述总结了当前关于人工智能对不确定多参数磁共振成像结果患者进行风险分层的证据，包括阐明关键的多中心研究计划，如前列腺影像-人工智能研究——这是一项将人工智能系统与放射科专家进行对比的全球基准研究。 方法：通过检索PubMed和Embase数据库（检索更新至2025年8月），使用"PI-RADS 3"、"影像组学"、"机器学习"、"深度学习"及"人工智能"等术语进行叙述性综述。纳入标准为以穿刺活检或长期随访作为参考标准，评估基于人工智能的PI-RADS 3类病灶临床意义前列腺癌预测的研究。同时纳入单中心与多中心研究，重点关注经过外部验证的模型。 结果：影像组学研究显示，从T2加权成像和扩散加权成像中提取的人工特征能够区分良性组织与临床意义前列腺癌，尤其在移行区，其受试者工作特征曲线下面积通常为0.75-0.82。深度学习方法（包括卷积神经网络和大规模表征学习框架）可获得更高性能，并能将良性活检率降低30%-40%。将基于影像的人工智能与前列腺特异性抗原密度等临床预测因子相结合的模型可进一步提升鉴别能力。迄今为止规模最大的国际基准研究——前列腺影像-人工智能研究（包含超过1万例磁共振检查）表明，尽管前瞻性验证仍有限，但先进的人工智能系统在不同扫描设备、中心及人群中检测临床意义前列腺癌的能力可与放射科专家相当甚至更优。 结论：人工智能通过减少不必要的穿刺活检、提高临床意义前列腺癌检出率以及降低阅片者间差异，展现出优化PI-RADS 3类病灶管理的强大潜力。要实现向临床常规应用的转化，仍需进行前瞻性多中心验证、统一影像采集协议，并将人工智能输出结果整合至临床工作流程中，同时建立明确的阈值标准、决策支持系统及安全网建议。
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