Neurology AI Standard of Care: Stroke Detection, Seizure Monitoring, and Liability

AI Reshapes Neurological Diagnosis and Care

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Neurology has emerged as one of the most dynamic frontiers for artificial intelligence in medicine. From AI algorithms that detect large vessel occlusions within seconds to continuous EEG monitoring systems that identify subclinical seizures, these technologies are fundamentally transforming how neurological conditions are diagnosed, triaged, and treated. But with this transformation comes unprecedented liability questions: When an AI system fails to detect a stroke and the patient misses the treatment window, who bears responsibility?

This guide examines the standard of care for AI use in neurology, the rapidly expanding landscape of FDA-cleared neurological AI devices, and the emerging liability framework for AI-assisted neurological care.

FDA-Cleared Neurology AI Devices

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Stroke Detection and Triage

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The largest category of neurological AI focuses on acute stroke detection and patient selection for intervention:

Major FDA-Cleared Stroke AI Devices (2024-2025):

2025 Notable Clearances:

• Felix NeuroAI System (Fasikl) - cleared June 2025 for neurological applications
• Rapid Obstructive Hydrocephalus (iSchemaView) - September 2025
• SwiftSight-Brain (AIRS Medical) - Brain MRI acceleration

Seizure Detection and EEG Analysis

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AI-powered EEG interpretation represents a growing category:

Clinical Applications:

• Continuous EEG monitoring in ICU settings
• Automated seizure detection and quantification
• Status epilepticus identification
• Interictal epileptiform discharge detection
• Sleep staging and analysis

Major FDA-Cleared EEG AI Devices:

Neurodegenerative Disease AI

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Emerging AI applications for Alzheimer’s, Parkinson’s, and other conditions:

Applications:

• Amyloid PET quantification
• Hippocampal volume measurement
• White matter hyperintensity quantification (Brain WMH - Quantib)
• Dopamine transporter imaging analysis
• Cognitive assessment AI tools

Recent Clearances:

• GBrain MRI (Galileo CDS) - Brain MRI analysis - August 2025
• GyriCalc (NeuroSpectrum) - Cortical analysis - July 2025
• Neuro Insight (Olea Medical) - Neuroimaging analysis - July 2025

Surgical Navigation and Robotics

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AI-enhanced neurosurgical systems:

The Liability Framework

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Time-Critical Decisions

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Neurological AI creates unique liability challenges due to the time-sensitive nature of many conditions:

The Treatment Window Problem:

• IV tPA window: 4.5 hours from symptom onset
• Mechanical thrombectomy: 6-24 hours depending on patient selection
• Every 15-minute delay reduces good outcomes by 4%
• AI delays or errors have immediate, severe consequences

The Triage Challenge:

“When AI stroke detection sends an alert, hospitals must be prepared to act. But what happens when the alert is a false positive, and resources are diverted from other emergencies? Or when a false negative delays life-saving intervention?”

Liability Allocation in Stroke AI

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Physician Responsibility:

• AI stroke alerts are advisory, not determinative
• Clinical correlation with presentation required
• Must understand AI limitations (posterior circulation, motion artifact)
• Document reasoning for agreeing or disagreeing with AI
• Cannot delegate final treatment decision to algorithm

Device Manufacturer Responsibility:

• Clear labeling of sensitivity/specificity limitations
• Training requirements for clinical staff
• Post-market surveillance for unexpected failures
• Timely communication of known limitations

Hospital/System Responsibility:

• Validation of AI performance in local population
• Integration into stroke protocols without delays
• Training programs for all relevant staff
• Quality monitoring and outcome tracking
• Ensure AI doesn’t replace neurological expertise

The “Black Box” Challenge in Neurology

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Explainability Issues:

• Why did AI miss this particular LVO?
• How does AI weight different CT findings?
• Can the decision be reconstructed for litigation?

Regulatory Response:

• FDA increasingly requiring transparency in algorithms
• Post-market real-world performance monitoring
• Requirement for clear intended use statements

Clinical Applications and Risk Areas

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Acute Ischemic Stroke

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The Stakes:

• 1.9 million neurons die per minute during stroke
• Time to treatment is the single most important modifiable factor
• AI can reduce door-to-needle time by 15-30 minutes
• Appropriate patient selection for thrombectomy is critical

AI Role:

• Automated LVO detection with mobile alerts
• ASPECTS scoring for treatment eligibility
• Perfusion imaging for extended window selection
• Notification of stroke team before patient arrives

Liability Concerns:

• False negatives: Missed LVO leading to disability or death
• False positives: Unnecessary catheterization with procedural risks
• Alert fatigue: Too many notifications leading to ignored alerts
• Over-reliance: Skipping clinical assessment based on AI output

Intracranial Hemorrhage

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AI Applications:

• ICH detection on non-contrast CT
• Hemorrhage volume estimation
• Expansion prediction algorithms
• Subdural vs. epidural differentiation

High-Stakes Environment: Emergency department settings where rapid triage decisions determine outcomes. AI can flag urgent findings, but misses can be catastrophic.

Case Pattern: Missed ICH A patient presents with headache and altered mental status. AI flags CT as “no acute intracranial hemorrhage.” Radiologist, seeing AI output, performs abbreviated review. Small subdural hematoma is missed. Patient deteriorates, requiring emergency surgery with poor outcome.

Seizure Monitoring

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AI Role:

• Continuous ICU EEG monitoring
• Detection of non-convulsive status epilepticus
• Seizure quantification for treatment titration
• Wearable seizure detection for outpatients

Liability Considerations:

• Missed non-convulsive seizures in critically ill patients
• False alarms leading to unnecessary treatment
• Wearable device failures in high-risk patients
• Alert fatigue in monitoring systems

Neurodegenerative Disease

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Emerging AI Applications:

• Early Alzheimer’s detection from imaging
• Parkinson’s progression monitoring
• Multiple sclerosis lesion tracking
• Prion disease pattern recognition

Unique Liability Issues:

• Prognostic AI creating anxiety without treatment options
• False positives for incurable conditions
• Privacy concerns with predictive neurological AI
• Duty to disclose AI-detected presymptomatic disease

American Academy of Neurology Guidance

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Position Statement on AI (2024)

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The AAN has provided guidance on AI integration in neurology:

Key Recommendations:

For Clinicians:

• AI should support, not replace, the neurological examination
• Maintain competency in skills AI may automate
• Understand AI limitations in atypical presentations
• Document AI use and clinical reasoning
• Report unexpected AI behavior or failures

For Institutions:

• Validate AI in local patient populations
• Ensure equity across demographic groups
• Integrate AI into clinical workflows thoughtfully
• Train all relevant staff on AI capabilities
• Monitor outcomes systematically

For Developers:

• Transparency in training data and methodology
• Clear labeling of intended use and limitations
• Diverse, representative training datasets
• Engagement with neurological societies
• Post-market surveillance commitment

Subspecialty Guidelines

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Stroke (American Stroke Association):

• AI stroke detection can reduce treatment delays
• Human interpretation remains essential
• Integration into stroke protocols required
• Quality metrics should include AI performance

Epilepsy (American Epilepsy Society):

• AI EEG interpretation aids efficiency
• Cannot replace fellowship-trained epileptologist review
• Wearable devices complement but don’t replace monitoring
• Patient education on device limitations essential

Standard of Care for Neurology AI

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What Reasonable Use Looks Like

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Pre-Implementation:

• Validate AI performance in your patient demographics
• Understand sensitivity/specificity in your setting
• Establish clear protocols for AI-positive and AI-negative results
• Train all relevant clinical staff
• Define escalation pathways

Clinical Use:

• AI recommendations inform but don’t determine treatment
• Clinical presentation guides interpretation of AI output
• Document reasoning when agreeing or disagreeing with AI
• Recognize limitations in specific populations (pediatric, posterior circulation)
• Maintain competency in manual interpretation

Quality Assurance:

• Track AI accuracy against clinical outcomes
• Monitor for demographic performance disparities
• Report adverse events to FDA MAUDE
• Regular performance reassessment
• Peer review of AI-assisted decisions

What Falls Below Standard

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Implementation Failures:

• Deploying AI without validation in local population
• Using stroke AI without integrated protocols
• No training for clinical staff
• Absence of quality monitoring

Clinical Failures:

• Treating AI output as definitive diagnosis
• Ignoring clinical presentation that contradicts AI
• Failing to escalate AI-negative cases with high clinical suspicion
• Over-reliance on AI in atypical presentations

Systemic Failures:

• No stroke team response protocol for AI alerts
• Alert fatigue due to poor implementation
• Failure to update for known AI limitations
• Ignoring FDA safety communications

Malpractice Considerations

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Emerging Case Patterns

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Neurology AI malpractice is an emerging area with several developing patterns:

Missed Stroke Claims:

• AI failed to detect LVO
• Treatment window passed before diagnosis
• Patient suffered preventable disability
• Allegations against device, hospital, physician, radiologist

ICH Detection Failures:

• AI reported no hemorrhage
• Physician relied on AI output without thorough review
• Delayed diagnosis of expanding hematoma
• Questions of physician vs. AI responsibility

Seizure Monitoring Failures:

• AI missed non-convulsive status epilepticus
• Patient suffered brain injury during undetected seizure activity
• Allegations of monitoring system inadequacy

The Stroke Case Framework

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Typical Elements:

1. Patient presents with stroke symptoms
2. AI stroke detection system deployed
3. AI either misses LVO (false negative) or delays notification
4. Treatment window passes
5. Patient has poor outcome
6. Multiple defendants: hospital, neurologist, radiologist, AI vendor

Defense Considerations:

• Was AI used according to labeling?
• Did physician apply independent clinical judgment?
• Were protocols followed?
• Was the condition detectable by the AI?
• Would outcome have differed with earlier detection?

Defense Strategies

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For Physicians:

• Documented clinical reasoning independent of AI
• Appropriate clinical correlation
• Recognition of AI limitations
• Compliance with manufacturer instructions
• Timely escalation despite negative AI

For Institutions:

• Validation documentation
• Training records
• Protocol compliance evidence
• Quality monitoring data
• Adverse event reporting compliance

For Manufacturers:

• FDA clearance documentation
• Proper labeling and warnings
• Training program adequacy
• Known limitations disclosure
• Post-market surveillance compliance

Telemedicine and Telestroke

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AI in Remote Stroke Care

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AI is particularly valuable in telestroke networks:

Applications:

• Automated LVO detection for spoke hospitals
• Real-time CT analysis during telemedicine consult
• Triage support for transfer decisions
• Mobile alerts to hub stroke team

Liability Considerations:

• Standard of care in remote settings
• Technology failures during critical transfers
• Communication breakdowns in distributed systems
• Responsibility allocation across facilities

The Rural Hospital Challenge

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Unique Issues:

• Limited specialist availability
• Greater reliance on AI support
• Transfer time to stroke centers
• Resource constraints for validation

Liability Implications:

• Higher AI reliance may be reasonable in resource-limited settings
• But fundamental clinical competencies still required
• Transfer protocols must account for AI limitations

Frequently Asked Questions

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Related Resources

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AI Liability Framework

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• AI Misdiagnosis Case Tracker, Diagnostic failure documentation
• AI Product Liability, Strict liability for AI systems
• Radiology AI Standard of Care, Diagnostic imaging AI

Healthcare AI

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• Healthcare AI Standard of Care, Overview of medical AI standards
• AI Medical Device Adverse Events, FDA MAUDE analysis
• Cardiology AI Standard of Care, Cardiovascular AI liability

Emerging Litigation

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• AI Litigation Landscape 2025, Overview of AI lawsuits