Autonomous AI in prostate cancer: the road ahead towards clinical implementation.
TL;DR
The findings highlight the need for evidence from large, multicenter, prospective trials and evaluation frameworks that reflect the consequences of clinical decision-making, as well as further exploration of safeguards to monitor and address mismatches between training data and incoming scans during deployment.
Abstract
Artificial Intelligence (AI) for detecting clinically significant prostate cancer (csPCa) on MRI has achieved diagnostic performance comparable to that of radiologists. By autonomously interpreting examinations, AI could improve workflow efficiency and help address increasing imaging demands and radiologist shortages. Despite this promise, autonomous AI has not been implemented in clinical practice. This narrative review explores remaining technical and societal barriers to deploying autonomous csPCa detection. We focus on three key domains: limitations in the current evidence base, safety issues and mitigation strategies, and the perspectives of patients and radiologists. Our findings highlight the need for evidence from large, multicenter, prospective trials and evaluation frameworks that reflect the consequences of clinical decision-making, as well as further exploration of safeguards to monitor and address mismatches between training data and incoming scans during deployment. Moreover, patients and radiologists show limited acceptance of autonomous AI, although this may improve with greater transparency, targeted education, and clearer guidelines on medico-legal responsibilities. Addressing these challenges is essential to the responsible deployment of autonomous AI and to realizing its efficiency gains in clinical practice.