Abstract 381: Artificial Intelligence as a Diagnostic Aid: A Systematic Review and Meta‐Analysis of Human‐AI Collaboration for Intracranial Aneurysm Detection

Abstract

Introduction Intracranial aneurysms (IAs) are a major cause of subarachnoid hemorrhage, yet accurate detection on CT angiography (CTA) remains challenging, particularly for small or complex lesions. While artificial intelligence (AI) algorithms have demonstrated high standalone accuracy, their true clinical value lies in augmenting human expertise. This systematic review and meta‐analysis evaluates the diagnostic accuracy of human‐AI collaboration compared with human‐only performance in detecting IAs. Methods We systematically reviewed meta‐analyses, multicenter retrospective validations, and prospective clinical trials reporting clinician performance with and without AI assistance. Eligible studies required reference standards from digital subtraction angiography (DSA) or expert consensus. Data on sensitivity, specificity, and area under the curve (AUC) were extracted. A pooled analysis was performed using random‐effects models to quantify improvements attributable to AI assistance. Secondary outcomes included reading time and clinician acceptance. Results Four representative studies encompassing more than 20,000 patients were analyzed. Across all datasets, AI assistance consistently enhanced clinician performance. In Gu et al. (2022), pooled clinician sensitivity increased from 0.84 to 0.92 with AI, accompanied by a rise in AUC from 0.85 to 0.93 and reduced interpretation time by ∼7 seconds per case. Din et al. (2023) reported similar improvements, with sensitivity rising from 0.83 to 0.90 and AUC from 0.88 to 0.91. Prospective real‐world validation by Hu et al. (2024) confirmed these gains: sensitivity improved from 0.59 to 0.825 and AUC from 0.787 to 0.909, with reading time reduced by ∼5 seconds per case. Wei et al. (2024) demonstrated external validation where clinician‐AI collaboration achieved an AUC of 0.93 versus 0.91 for radiology reports, while maintaining efficiency with a mean processing time of 1.7 minutes per scan. Pooled analysis across studies yielded a mean sensitivity improvement of +8% (95% CI: +6‐10%) and AUC gain of +0.06, both statistically significant. Clinician adoption rates exceeded 90% in prospective settings, underscoring strong acceptance of AI tools. Conclusion Human‐AI collaboration significantly improves the diagnostic accuracy of intracranial aneurysm detection on CTA, with consistent gains in sensitivity, AUC, and efficiency across retrospective and prospective studies. AI assistance reduces missed diagnoses while maintaining or improving workflow efficiency. These findings support the integration of AI as a diagnostic aid rather than a standalone tool. Future work should focus on harmonized reporting standards, prospective multicenter trials, and evaluation of long‐term clinical outcomes to fully establish AI's role in routine neuroradiological practice.

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