Agentic AI for Prostate Cancer: A Vision for Multimodal Clinical Intelligence

Comprehensive Summary

This paper describes a decision-support framework for prostate cancer that integrates MRI findings, pathology results, PSA kinetics, longitudinal clinical data, and patient preferences into a single system. It addresses the limitations of current AI models, which rely on isolated data and do not reflect the complex, multidisciplinary nature of prostate cancer decision-making. Agentic AI uses coordinated components to combine these inputs, improving consistency and providing transparent reasoning for decisions such as biopsy, active surveillance, or treatment. It enables dynamic risk assessment by continuously updating a patient’s risk of progression using new clinical information, allowing earlier detection of disease changes, more precise stratification, and better timing of intervention. By incorporating patient preferences and quality-of-life considerations, it supports more personalized, patient-centered care while helping to reduce both overtreatment and undertreatment.

Outcomes and Implications

This paper describes a unified decision-support framework that integrates imaging, pathology, longitudinal clinical data, and patient preferences. This system allows for better consistency and transparency in decision-making. Agentic AI can be used for earlier detection of disease progression, more precise patient stratification, and better timing of intervention. By allowing for individual preferences and quality-of-life considerations to be implemented, patient-centered care is able to be enhanced and minimize unnecessary interventions.