Intracranial aneurysms can rupture and cause life-threatening subarachnoid hemorrhage. As CT angiography and MR angiography become more common in routine care, clinicians are detecting more unruptured aneurysms. The difficult cases remain familiar ones: small aneurysms, unusual locations, and complex vascular anatomy. AI may help radiologists find small or tiny aneurysms and reduce differences between readers. But clinical use requires a harder question than model accuracy on a test dataset: does the tool improve workflow, support safer decisions, and help patients?
In a new Perspective in Science Bulletin , Longjiang Zhang and colleagues examine how AI for intracranial aneurysm detection should be evaluated before broad clinical use. They describe a four-stage pathway covering analytical validation, reader performance evaluation, prospective implementation evaluation, and surveillance after deployment.
The first step is lesion-level testing in the clinical setting where the system is meant to work. According to the article, AI systems tend to perform better for aneurysms larger than 5 mm. Performance falls for aneurysms measuring 3 to 5 mm and for those below 3 mm. That makes small aneurysms a critical test case, because extra detections may come with false positives and overdiagnosis.
The second step is to study what happens when doctors read images with AI support. Multi-reader, multi-case studies can estimate the average effect of AI assistance while showing how results differ across readers, cases, institutions, and experience levels. The authors also warn about automation bias: clinicians may trust AI output too readily and accept incorrect prompts. Sham AI designs can help show whether a change in doctor behavior comes from real diagnostic value or simply from exposure to an AI-like interface.
The third step is prospective implementation evaluation. Randomized studies can test whether improved detection changes clinical management, downstream resource use, treatment-related events, aneurysm-related outcomes, quality of life, and cost effectiveness. This is especially important for aneurysms smaller than 3 mm, where finding more lesions may also mean more false positives and unnecessary follow-up.
The last step is surveillance after deployment. A locked algorithm can still behave differently over time as scanner platforms, acquisition protocols, reconstruction methods, and patient populations change. Monitoring should therefore include technical performance and the clinical actions triggered by AI, such as follow-up vascular imaging, confirmatory testing, specialist consultation, treatment, and resource use linked to false-positive findings.
The article’s main point is that imaging AI should not be evaluated only by asking whether the model is accurate. For intracranial aneurysm detection, the more useful question is whether the tool provides net clinical benefit in a specific care setting.
Science Bulletin