The Promise and the Gap
A comprehensive review published in Nature Reviews Neurology highlights a critical disconnect in neurology: while artificial intelligence (AI) has demonstrated remarkable potential in research settings, its transition into routine clinical care remains slow and uneven. The paper surveys the current landscape of AI applications in neurology, including automated EEG interpretation for epilepsy monitoring, AI-powered brain MRI segmentation (notably SynthSeg, which can analyze scans of any contrast and resolution without retraining), seizure detection algorithms, and large language models tailored for neurological conditions. These tools promise to enhance diagnostic accuracy, reduce clinician workload, and improve patient outcomes, yet their real-world adoption lags far behind research advancements.
Regulatory and Evaluation Hurdles
The review delves into the evolving regulatory environment, discussing FDA guidance on AI-enabled medical device software lifecycle management, the EU AI Act, and emerging frameworks such as PROBAST+AI for assessing bias in AI prediction models and the GREENBEAN checklist for EEG-based biomarker studies. Despite these efforts, key barriers persist: a lack of standardized evaluation frameworks, limited generalizability of AI models across diverse clinical settings, poor reimbursement pathways (even as radiology dominates FDA-cleared AI approvals), and an urgent need for robust quality assessment tools. The authors argue that without harmonized regulatory standards, real-world validation studies, and seamless integration with existing clinical workflows, even the most promising neurology-specific AI tools will remain confined to research labs.
A Path Forward
To bridge the gap from research to routine clinical use, the review calls for a coordinated effort among researchers, clinicians, regulators, and payers. Real-world validation studies that test AI models across multiple institutions and patient populations are essential to ensure generalizability. Additionally, developing clear reimbursement models and embedding AI tools into existing electronic health record systems could accelerate adoption. The authors conclude that while neurology-specific AI has shown immense promise, achieving widespread clinical impact will require a shift from isolated innovation to integrated, evidence-based deployment.
