AI in Neurology
The mature use of AI in neurology is acute stroke: tools that flag large vessel occlusions and bleeds on CT and speed up transfers. EEG and seizure detection is next, with bedside rapid-EEG devices. Most other uses, such as dementia prediction from imaging or speech, are still research.
What AI does in Neurology
Alerts the neurologist or interventionist within minutes of the scan.
Examples: Viz.ai, RapidAI, Brainomix 360 Stroke, Qure.ai qER
Auto-processes perfusion and early-change scores for thrombectomy decisions.
Examples: RapidAI, Viz CTP, Brainomix
A headband EEG with AI flags status epilepticus in ICU or ED.
Examples: Ceribell
Flags bleeds and mass effect on emergency head CT.
Examples: Qure.ai qER
Models help find seizure onset zones and automate EEG review.
Examples: Academic models; see 2025 epilepsy review
Models use imaging, speech or wearable data to predict disease. Few are validated in practice.
Examples: Research models
routine = in everyday use in many centres · early adoption = in use at some sites, evidence still building · research = studies only
Key ideas for this specialty
New to the basics? Start with the theory lessons.
Tools
LVO, CT perfusion, haemorrhage and aneurysm detection with team alerts.
Regulatory status: Site states more than 50 FDA-cleared algorithms; EU customers noted
Neurovascular modules for stroke, aneurysm and haemorrhage.
Regulatory status: Site states 30 FDA-cleared modules overall
AI reading of NCCT, CTA, CTP and MRI for stroke.
Regulatory status: Site lists Brainomix 360 Stroke as CE marked
Head CT and trauma AI; Indian company with stroke work in Indian hospitals.
Regulatory status: Site states FDA cleared and CE marked
Point-of-care EEG with AI seizure detection.
Regulatory status: Site does not state clearance
A listing here is not an endorsement. Ask any vendor for its CDSCO licence and class before clinical use.
Where to learn
Hands-on diagnosis, prognosis and treatment models; needs Python and some deep learning background.
Interviews with people building and testing medical AI; hosts are Raj Manrai and Andrew Beam.
Shows the AAN's policy position on AI bias and access.
India's national ethics framework; useful before you put any AI tool into a thesis or ward workflow.
Five short courses that teach clinicians how to judge AI tools; CME available for physicians.
Indian course built for clinicians, from IIT Madras faculty.
Do it yourself
Download CHB-MIT from PhysioNet. Cut recordings into 10-second windows, extract band power and train a classifier for seizure vs non-seizure. Look at false alarms per hour.
Open the dataset or tool →Pick one stroke AI paper from the 2025 review below. Write down the sensitivity, false alert rate and which hospital data it used.
Open the dataset or tool →Use public or properly de-identified data only, and get ethics approval before any project on your own patients.
Papers worth reading
- Applications of artificial intelligence in acute stroke imaging Artificial Intelligence in Health, 2025
- Artificial Intelligence in Epilepsy: A Systemic Review Journal of Epilepsy Research, 2025
- Artificial Intelligence in Stroke Imaging: A Comprehensive Review The Eurasian Journal of Medicine, 2023
Societies and guidance
Policy priorities include AI that does not worsen disparities or bias; AAN endorses AMA principles for augmented intelligence.
Links checked 3 October 2026. “Not listed” means we could not confirm the detail, not that it is absent. Educational material, not clinical advice.