AI and human interactions in prostate cancer diagnosis using MRI.

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. KEY POINTS: Question Single-reader prostate MRI interpretations have reader variability and missed cancer limitations. This report explores how prospective AI integration can improve diagnostic accuracy and workflow efficiency. Findings From decision support to autonomous analyses, AI workflows can improve cancer detection and streamline workflows. Mitigating errors requires tailored performance and high accuracy for greater autonomy. Clinical relevance Calibrated AI-radiologist collaborations can enhance prostate cancer diagnosis by improving accuracy and efficiency while reducing unnecessary biopsies and missed cancers. Prospective research evaluating the safety and efficacy of AI deployment is needed for responsible and beneficial AI adoption.