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Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for
Marina Ramzy Mourid1, Hamza Irfan2, Malik Olatunde Oduoye3
1Faculty of Medicine Alexandria University Alexandria Egypt.
Insights
Artificial intelligence (AI) shows significant promise in diagnosing and managing pediatric epilepsy, achieving high accuracy in detection. Addressing ethical concerns like data privacy and bias is crucial for responsible implementation in child epilepsy care.
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.
Background And Aim:
Epilepsy is a major neurological challenge, especially for pediatric populations. It profoundly impacts both developmental progress and quality of life in affected children. With the advent of artificial intelligence (AI), there's a growing interest in leveraging its capabilities to improve the diagnosis and management of pediatric epilepsy. This review aims to assess the effectiveness of AI in pediatric epilepsy detection while considering the ethical implications surrounding its implementation.
Methodology:
A comprehensive systematic review was conducted across multiple databases including PubMed, EMBASE, Google Scholar, Scopus, and Medline. Search terms encompassed "pediatric epilepsy," "artificial intelligence," "machine learning," "ethical considerations," and "data security." Publications from the past decade were scrutinized for methodological rigor, with a focus on studies evaluating AI's efficacy in pediatric epilepsy detection and management.
Results:
AI systems have demonstrated strong potential in diagnosing and monitoring pediatric epilepsy, often matching clinical accuracy. For example, AI-driven decision support achieved 93.4% accuracy in diagnosis, closely aligning with expert assessments. Specific methods, like EEG-based AI for detecting interictal discharges, showed high specificity (93.33%-96.67%) and sensitivity (76.67%-93.33%), while neuroimaging approaches using rs-fMRI and DTI reached up to 97.5% accuracy in identifying microstructural abnormalities. Deep learning models, such as CNN-LSTM, have also enhanced seizure detection from video by capturing subtle movement and expression cues. Non-EEG sensor-based methods effectively identified nocturnal seizures, offering promising support for pediatric care. However, ethical considerations around privacy, data security, and model bias remain crucial for responsible AI integration.
Conclusion:
While AI holds immense potential to enhance pediatric epilepsy management, ethical considerations surrounding transparency, fairness, and data security must be rigorously addressed. Collaborative efforts among stakeholders are imperative to navigate these ethical challenges effectively, ensuring responsible AI integration and optimizing patient outcomes in pediatric epilepsy care.
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