The Future of AI in Seizure Management for Dementia Patients

The Hidden Connection

The relationship between seizures and dementia represents one of neurology’s most challenging frontiers. Patients with Alzheimer’s disease are up to ten times more likely to experience seizures than the general population, while these seizures—many of them subclinical—actively accelerate cognitive decline (Vossel et al., 2021).

Recent groundbreaking research from UCLA Health has shed light on this relationship. Dr. Keith Vossel’s team found that silent epileptic activity occurs in more than 40% of Alzheimer’s patients—significantly higher than the 20% who experience overt seizures. Their latest study revealed that high-frequency oscillations (HFOs) occur at rates two to three times higher in Alzheimer’s patients compared to cognitively normal individuals, paving the way for new detection and treatment methods (Shandilya et al., 2025).

This complex relationship requires advanced detection techniques that human observation alone can’t provide—thereby positioning artificial intelligence uniquely to transform care in this area.

AI-Enhanced Detection: Beyond Traditional Monitoring

Seizure detection in dementia patients primarily relies on observable symptoms or scheduled EEG readings, missing many subclinical events. Research at UCLA demonstrated that magnetoencephalography (MEG) screening for HFOs takes just 10 minutes and offers superior detection compared to traditional EEG (Shandilya et al., 2025).

AI could dramatically enhance these capabilities through:

Advanced Signal Processing: AI algorithms might identify HFOs and other biomarkers with higher sensitivity than existing methods, detecting patterns that are too subtle for visual inspection.

Multimodal Monitoring: By integrating data from wearable devices, ambient sensors, and clinical assessments, AI could detect seizure precursors through variations in movement patterns, heart rate variability, and sleep quality (Vossel et al., 2016).

Healthcare facilities that adopt these strategies could greatly enhance seizure detection rates, facilitate more timely interventions, and decrease emergency transfers.

From Reactive to Proactive: The Predictive Revolution

Perhaps the most promising frontier is the shift from reactive management to proactive prevention through predictive analytics:

Seizure Forecasting: By analyzing physiological data alongside environmental factors, AI models could identify periods of elevated seizure risk hours or days in advance, enabling preventive interventions.

Biomarker Integration: AI systems could monitor HFOs and other biomarkers, identifying which specific patterns most strongly predict seizure risk in individual patients.

Progression Modeling: Advanced algorithms could track the relationship between seizure activity and cognitive decline trajectories, helping clinicians understand how various interventions affect both conditions.

Personalized Treatment Optimization

The UCLA research indicates that low doses of levetiracetam can enhance spatial memory and problem-solving skills in Alzheimer’s patients experiencing epileptic activity. AI could further improve treatment personalization through the following:

Medication Response Prediction: AI models could predict individual responses to specific medications, balancing seizure control against cognitive side effects.

Dose Optimization: Reinforcement learning algorithms could identify optimal medication dosing strategies for individual patients, minimizing side effects while maximizing therapeutic benefit.

Economic Implications

For healthcare executives, the potential financial impacts could be significant:

• Acute Care Utilization: Early detection and intervention systems have the potential to reduce seizure-related emergency department visits and hospitalizations.
• Hospital Efficiency: More precise treatment plans may help reduce the length of stay for affected patients.
• Workflow Optimization: Automated monitoring technologies could improve clinical workflow efficiency.
• Preventive Care: Earlier identification of seizure risk through advanced screening might enable more targeted interventions before acute events occur.

The Technical Horizon

Several approaches show particular promise:

Multimodal Data Integration: Future systems will combine data from wearables, medical records, imaging results, and environmental sensors to create comprehensive risk profiles and intervention recommendations.

Care Coordination Platforms: AI-enabled platforms could ensure all team members—from neurologists to frontline caregivers—have access to synchronized, actionable information tailored to their role.

The Human-AI Partnership

Despite these technological advances, the human element remains irreplaceable. The most effective implementations will maintain a crucial balance: AI handles data processing and pattern recognition, while human clinicians provide support, contextual understanding, and ethical judgment (Lai et al., 2021).

Ethical Considerations

As we advance this technology, we must address:

1. Privacy Protections: Continuous monitoring raises important privacy concerns for vulnerable populations.
2. Consent Processes: Establishing meaningful consent when patients may have fluctuating capacity.
3. Algorithm Transparency: Ensuring clinicians and families understand the basis for AI recommendations.
4. Access Equity: Ensuring these advances benefit diverse populations across socioeconomic boundaries.

The Path Forward

For healthcare leaders preparing for this future, consider the following:

1. Exploring Advanced Diagnostics: Investigating the potential role of MEG screening for HFOs.
2. Strengthening Cross-Disciplinary Collaboration: Improving coordination between neurology and dementia care teams.
3. Building Data Infrastructure: Ensuring readiness to integrate future AI solutions.

Conclusion

The intersection of seizure disorders and dementia has long been complex to address effectively. Groundbreaking research is yielding fresh insights into the mechanisms connecting these conditions, while artificial intelligence presents extraordinary opportunities to revolutionize care through improved detection, personalized treatment, and integrated care systems.

The convergence of neuroscience and data science promises a future where seizures in dementia patients become more manageable and less disruptive to cognitive function—a future worth pursuing for the millions affected by these challenging conditions.

Ready to transform dementia care with AI-driven seizure management? Join the conversation and explore innovative solutions today! Sign up for Seagull Health’s newsletter.

References

Lai, Y., Kankanhalli, A., & Ong, D. (2021). Human-AI Collaboration in Healthcare: A Review and Research Agenda. Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2021.046

Tang, J., El Atrache, R., Yu, S., Asif, U., Jackson, M., Roy, S., Mirmomeni, M., Cantley, S., Sheehan, T., Schubach, S., Ufongene, C., Vieluf, S., Meisel, C., Harrer, S., & Loddenkemper, T. (2021). Seizure detection using wearable sensors and machine learning: Setting a benchmark. Epilepsia, 62(8), 1807–1819. https://doi.org/10.1111/epi.16967

Vossel, K. A., Ranasinghe, K. G., Beagle, A. J., Mizuiri, D., Honma, S. M., Dowling, A. F., Darwish, S. M., Van Berlo, V., Barnes, D. E., Mantle, M., Karydas, A. M., Coppola, G., Roberson, E. D., Miller, B. L., Garcia, P. A., Kirsch, H. E., Mucke, L., & Nagarajan, S. S. (2016). Incidence and Impact of Subclinical Epileptiform Activity in Alzheimer’s Disease. Annals of Neurology, 80(6), 858–870. https://doi.org/10.1002/ana.24794

Shandilya, M. C. V., Addo-Osafo, K., Ranasinghe, K. G., Shamas, M., Staba, R., Nagarajan, S. S., & Vossel, K. (2025). High-frequency oscillations in epileptic and non-epileptic Alzheimer’s disease patients and the differential effect of levetiracetam on the oscillations. Brain Communications, 7(1), fcaf041. https://doi.org/10.1093/braincomms/fcaf041