Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare
ABSTRACT 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...
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Wiley
2025-01-01
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| Series: | Health Science Reports |
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| Online Access: | https://doi.org/10.1002/hsr2.70372 |
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| author | Marina Ramzy Mourid Hamza Irfan Malik Olatunde Oduoye |
| author_facet | Marina Ramzy Mourid Hamza Irfan Malik Olatunde Oduoye |
| author_sort | Marina Ramzy Mourid |
| collection | DOAJ |
| description | ABSTRACT 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. |
| format | Article |
| id | doaj-art-3bb8ccc25e934c37a596277de673742b |
| institution | Kabale University |
| issn | 2398-8835 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Science Reports |
| spelling | doaj-art-3bb8ccc25e934c37a596277de673742b2025-01-29T03:42:39ZengWileyHealth Science Reports2398-88352025-01-0181n/an/a10.1002/hsr2.70372Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for WelfareMarina Ramzy Mourid0Hamza Irfan1Malik Olatunde Oduoye2Faculty of Medicine Alexandria University Alexandria EgyptDepartment of Medicine Shaikh Khalifa Bin Zayed Al Nahyan Medical and Dental College Lahore PakistanDepartment of Research The Medical Research Circle (MedReC) Goma Democratic Republic of the CongoABSTRACT 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.https://doi.org/10.1002/hsr2.70372artificial intelligencediagnosisethical considerationsethical implicationsneurological disorderpatient outcomes |
| spellingShingle | Marina Ramzy Mourid Hamza Irfan Malik Olatunde Oduoye Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare Health Science Reports artificial intelligence diagnosis ethical considerations ethical implications neurological disorder patient outcomes |
| title | Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare |
| title_full | Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare |
| title_fullStr | Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare |
| title_full_unstemmed | Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare |
| title_short | Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare |
| title_sort | artificial intelligence in pediatric epilepsy detection balancing effectiveness with ethical considerations for welfare |
| topic | artificial intelligence diagnosis ethical considerations ethical implications neurological disorder patient outcomes |
| url | https://doi.org/10.1002/hsr2.70372 |
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