Artificial Intelligence (AI) is increasingly being explored in otolaryngology (ENT) for applications such as diagnosis, treatment management, image enhancement, and voice analysis. Neural networks (NNs), particularly convolutional neural networks (CNNs), have shown high accuracy in tasks like image segmentation and classification. However, challenges remain in standardizing AI protocols, ensuring data quality, and addressing ethical concerns. This review discusses various types of NNs, their applications in ENT, and the challenges in their implementation. CNNs are widely used for medical imaging due to their ability to process complex data and achieve high accuracy. Other NN types, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, are also applied in tasks like voice analysis and prediction. Despite their potential, AI applications in ENT face issues such as data homogeneity, limited dataset size, and the need for multicenter validation. The review emphasizes the importance of standardization, ethical considerations, and interdisciplinary collaboration in advancing AI in ENT. Future research should focus on improving AI accuracy, ensuring data consistency, and promoting multicenter studies to enhance the reliability and generalizability of AI applications in ENT.Artificial Intelligence (AI) is increasingly being explored in otolaryngology (ENT) for applications such as diagnosis, treatment management, image enhancement, and voice analysis. Neural networks (NNs), particularly convolutional neural networks (CNNs), have shown high accuracy in tasks like image segmentation and classification. However, challenges remain in standardizing AI protocols, ensuring data quality, and addressing ethical concerns. This review discusses various types of NNs, their applications in ENT, and the challenges in their implementation. CNNs are widely used for medical imaging due to their ability to process complex data and achieve high accuracy. Other NN types, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, are also applied in tasks like voice analysis and prediction. Despite their potential, AI applications in ENT face issues such as data homogeneity, limited dataset size, and the need for multicenter validation. The review emphasizes the importance of standardization, ethical considerations, and interdisciplinary collaboration in advancing AI in ENT. Future research should focus on improving AI accuracy, ensuring data consistency, and promoting multicenter studies to enhance the reliability and generalizability of AI applications in ENT.