Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for...

Area of Science:

• Neurology
• Medical Informatics
• Artificial Intelligence

Background:

• Pediatric epilepsy presents significant challenges to child development and quality of life.
• Artificial intelligence (AI) offers novel approaches for improving diagnosis and management of this condition.

Purpose of the Study:

• To review the effectiveness of AI in pediatric epilepsy detection.
• To consider the ethical implications of AI implementation in pediatric epilepsy care.

Main Methods:

• A systematic review of multiple databases (PubMed, EMBASE, Scopus, etc.) was performed.
• Search terms included "pediatric epilepsy," "artificial intelligence," and "ethical considerations."
• Publications from the last decade were analyzed for AI efficacy and ethical considerations.

Main Results:

• AI systems demonstrate high diagnostic accuracy, comparable to clinical assessments (e.g., 93.4% for AI decision support).
• EEG-based AI shows high sensitivity and specificity for detecting interictal discharges; neuroimaging reaches up to 97.5% accuracy.
• Deep learning and non-EEG sensors enhance seizure detection, but ethical issues like privacy and bias require attention.

Conclusions:

• AI holds substantial potential for advancing pediatric epilepsy care.
• Addressing ethical considerations, including transparency, fairness, and data security, is paramount for responsible AI integration.
• Collaborative efforts are essential to optimize patient outcomes in pediatric epilepsy management.