Multimodal Artificial Intelligence Driving Precision Diagnosis and Treatment of Otolaryngologic Diseases: Key Challenges and Future Directions
Drawing on the diagnostic and therapeutic characteristics of otology, rhinology, laryngology, and head and neck oncology, this article summarizes representative applications of multimodal artificial intelligence in otolaryngology–head and neck surgery, analyzes translational issues including data standards and cross-modal alignment, missing modalities and model generalization, privacy protection and multicenter collaboration, interpretability, clinical evidence, and workflow integration, and proposes establishing specialty data standards suited to clinical practice in China, building a staged multicenter validation system, forming a human–machine collaboration model supervised by specialty physicians, and cautiously advancing general and specialty large models toward multimodal clinical ap