Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Sep 10, 2025
Date Accepted: Mar 7, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Vision-based AI technologies for Epilepsy Monitoring: A comprehensive Taxonomy and Future Directions
ABSTRACT
Background:
Artificial Intelligence (AI) technologies for vision-based epilepsy monitoring are advancing rapidly in healthcare. Despite a growing body of research utilizing various video data sources and analytical approaches, no comprehensive framework exists to classify these emerging technologies.
Objective:
The purpose of this study is to develop and validate a comprehensive taxonomy for categorizing AI technologies in vision-based epilepsy monitoring. We aim to provide fundamental insights into AI-based approaches in epilepsy care based on visual data processing. The proposed taxonomy specifies key properties of these technologies. This will contribute to a more robust knowledge base for further developing advanced healthcare solutions in epilepsy care.
Methods:
Guided by an extended taxonomy development framework, our taxonomy was developed within six iterative cycles based on data from theory and practice. We conducted a structured literature review, a market analysis and a practical validation of the taxonomy using market-ready solutions. To ensure validity and practical relevance, domain experts evaluated the taxonomy using a Delphi technique.
Results:
The final taxonomy comprises 23 dimensions, e.g., target group, seizure classification and performance metrics, with 102 defining characteristics. It enables a systematic and transparent classification of existing and future AI technologies in vision-based epilepsy monitoring solutions. In addition, the study identifies major limitations of current vision-based systems and synthesizes eight implications for future research and practical implementation. Key challenges are highlighted in the areas of standardization, system robustness, and real-time applicability.
Conclusions:
By providing a structured overview of the current landscape and identifying critical research gaps, this study provides a solid conceptual foundation for AI technologies in vision-based epilepsy monitoring and contributes to the ongoing development of this field. Furthermore, the specified implications and future directions offer a practical outlook to facilitate further research of AI technologies in vision-based epilepsy monitoring.
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Copyright
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