AI-Based Diagnostic Imaging Validation

Abstract

AI-based diagnostic imaging systems have demonstrated radiologist-level or superior performance across multiple imaging modalities and clinical tasks in controlled research settings, yet their real-world clinical validation remains a critical bottleneck to deployment. The gap between algorithm performance on curated benchmark datasets and operational effectiveness in diverse clinical environments -- with varying scanner hardware, patient demographics, image quality, and clinical workflows -- represents a systematic challenge that the field has been slow to address rigorously. We evaluated five validation frameworks for AI diagnostic imaging systems across 186,424 imaging studies from six European radiology departments (Tallinn, Stockholm, Rome, Zurich, Paris, and Berlin) from 2018 to 2023, covering chest X-ray, CT, MRI, and mammography. We compared: retrospective benchmark validation, prospective reader study comparison, silent deployment with delayed outcome linkage, active clinical deployment with clinician override tracking, and federated multi-site validation. Our AI Imaging Validation Score (AIVS) integrates diagnostic accuracy, generalisability across sites and scanners, workflow integration, clinical outcome impact, and bias assessment. Federated multi-site validation achieved the highest AIVS (0.928) by demonstrating robust performance across heterogeneous imaging environments while exposing performance gaps invisible to single-site validation, while active clinical deployment with override tracking (AIVS 0.912) provided the most direct evidence of real-world clinical utility through prospective outcome measurement.