Prostate MRI is a well-established AI use case, with 8 CE-marked applications already on the market. But for clinicians, a key question remains: how can these systems be safely and effectively integrated into diagnostic workflows?
This special report by Padhani and Papanikolaou outlines six distinct AI-enabled workflow models. These range from AI acting as a “second reader” to catch missed cancers, to high-confidence triage pathways where low-risk cases are fast-tracked or filtered out entirely. Each approach presents its own trade-offs in terms of sensitivity, specificity, clinical oversight, and efficiency gains. The authors also highlight critical risks, including automation bias and over-reliance on AI outputs, which could undermine clinical judgment.
For hospitals looking to implement prostate AI, this paper offers more than just technical guidance, it provides a clear framework for evaluating which workflow fits which clinical setting, and what is required to ensure safe and effective AI adoption.
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AI and human interactions in prostate cancer diagnosis using MRI
European Radiology, 2025
Abstract
This special report explores the integration of artificial intelligence (AI) into prostate MRI workflows to address limitations associated with single-reader interpretations, such as inter-reader variability and diagnostic errors. We review various AI-integrated workflow strategies, from AI-assisted decision support to fully autonomous analysis, examining their benefits and challenges. AI can act as a second reader, enhancing detection sensitivity and reducing false negatives or pre-screen cases for efficient triage, thereby optimising radiologist workload. Key advantages include the potential for improved lesion detection, streamlined workflows, and reduced reporting times. However, challenges such as automation bias and the potential for inaccurate AI outputs require careful consideration and mitigation strategies. The suitability of different AI workflows is dependent on the clinical context and desired performance, with high sensitivity and negative predictive value crucial for rule-out scenarios and high specificity and positive predictive value essential for rule-in scenarios. Increased AI autonomy mandates a higher performance benchmark. The need for rigorous prospective validation studies assessing AI safety and effectiveness in real-world clinical settings is emphasised. Furthermore, the complex dynamics of human-AI interaction, encompassing positive and negative consequences, warrant further investigation. Ultimately, the strategic implementation of collaborative AI-radiologist workflows has the potential to enhance diagnostic accuracy and efficiency and reduce missed cancers, leading to more timely and appropriate patient care.