AI in Acute Neurology: Transforming Patient Care

Introduction: AI in Acute Neurology

The integration of artificial intelligence (AI) in acute neurology is revolutionizing how we diagnose and treat life-threatening conditions such as stroke, traumatic brain injury (TBI), epilepsy, and intracranial hemorrhage (ICH). With growing evidence supporting AI’s diagnostic and prognostic capabilities, its role in emergency neurology continues to expand.

We take a look at recent meta-analyses reporting pooled results on AI's ability to assist acute neurologists at delivering care for their patients.

Enhanced Stroke Care with AI

AI algorithms now assist in detecting acute ischemic stroke lesions on diffusion-weighted MRI with approximately 93% sensitivity and specificity​. These tools can match or surpass expert radiologists in early ischemic change detection via AI-based ASPECTS scoring on CT. Moreover, machine learning models demonstrate a pooled AUC of ~0.87 in predicting stroke outcomes at 3–6 months—supporting clinicians in decision-making during the most critical stages of care.

AI in TBI: Predicting Outcomes with Precision

In the realm of traumatic brain injury, AI helps forecast mortality and long-term outcomes with high accuracy. Across studies, machine learning models achieve >80% accuracy, significantly outperforming traditional regression-based methods​.

AI-Enhanced Epilepsy Diagnosis

Detecting interictal epileptiform discharges (IEDs) on EEGs has long been a challenge. AI models now identify these patterns with 85% sensitivity and 69% specificity on external validation, contributing to faster, more accurate epilepsy diagnoses—a critical step, especially in drug-resistant cases.

AI for Intracranial Hemorrhage (ICH) Detection

Deep learning algorithms excel at spotting intracranial hemorrhage on non-contrast CT scans. Meta-analyses reveal pooled sensitivity of ~92%, specificity of ~94%, with a summary AUC of 0.96, offering rapid triage and treatment initiation potential in emergency settings (Figure 1.).

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^{Figure 1. In the Hierarchical Summary Receiver Operating Characteristic (HSROC) curve, the summary point has identical sensitivity and specificity values to corresponding measures in the forest plots, which revealed an ischaemic stroke detection sensitivity of 93% (CI 86–96%) and specificity of 93% (CI 84–96%).}

Integration Challenges & The Role of Neurologists

Despite promising outcomes, integrating AI into clinical neurology isn’t without hurdles:

• Data infrastructure must support secure, high-quality input.
• Model generalizability remains a concern across diverse populations.
• Clinical validation is essential before deployment.‍‍

Neurologists are central to this evolution - guiding data annotation, model interpretation, and ensuring ethical, patient-centered use of AI in clinical workflows.

The Future of AI in Acute Neurology

The promise of AI in acute neurology is clear: faster diagnoses, personalized prognoses, and better outcomes. But realizing its full potential demands thoughtful implementation, continuous validation, and clinician involvement at every step.