International journal of pediatric otorhinolaryngology · 2025
Artificial intelligence in pediatric otolaryngology: A state-of-the-art review of opportunities and pitfalls.
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Navarathna Nithya, Kanhere Adway, Gomez Charlyn, Isaiah Amal
Аннотация
BACKGROUND: Artificial Intelligence (AI) and machine learning (ML) have transformative potential in enhancing diagnostics, treatment planning, and patient management. However, their application in pediatric otolaryngology remains limited as the unique physiological and developmental characteristics of children require tailored AI applications, highlighting a gap in knowledge.
PURPOSE: To provide a narrative review of current literature on the application of AI in pediatric otolaryngology, highlighting knowledge gaps, associated challenges and future directions.
RESULTS: ML models have demonstrated efficacy in diagnosing conditions such as otitis media, adenoid hypertrophy, and pediatric obstructive sleep apnea through deep learning-based image analysis and predictive modeling. AI systems also show potential in surgical settings such as landmark identification during otologic surgery and prediction of middle ear effusion during tympanostomy tube placement. Telemedicine solutions and large language models have shown potential to improve accessibility to care and patient education. The principal challenges include flawed generalization of adult training data and the relative lack of pediatric data.
CONCLUSIONS: AI holds significant promise in pediatric otolaryngology. However, its widespread clinical integration requires addressing algorithmic bias, enhancing model interpretability, and ensuring robust validation across pediatric population. Future research should prioritize federated learning, developmental trajectory modeling, and psychosocial integration to create patient-centered solutions.
PURPOSE: To provide a narrative review of current literature on the application of AI in pediatric otolaryngology, highlighting knowledge gaps, associated challenges and future directions.
RESULTS: ML models have demonstrated efficacy in diagnosing conditions such as otitis media, adenoid hypertrophy, and pediatric obstructive sleep apnea through deep learning-based image analysis and predictive modeling. AI systems also show potential in surgical settings such as landmark identification during otologic surgery and prediction of middle ear effusion during tympanostomy tube placement. Telemedicine solutions and large language models have shown potential to improve accessibility to care and patient education. The principal challenges include flawed generalization of adult training data and the relative lack of pediatric data.
CONCLUSIONS: AI holds significant promise in pediatric otolaryngology. However, its widespread clinical integration requires addressing algorithmic bias, enhancing model interpretability, and ensuring robust validation across pediatric population. Future research should prioritize federated learning, developmental trajectory modeling, and psychosocial integration to create patient-centered solutions.