Yuhuangding Hospital

This article systematically reviews the current applications, main problems, and future directions of medical AI image technology in the precision diagnosis and treatment of head and neck tumors, noting that the technology enables precise quantification of imaging features and opens new paths for early identification, efficacy evaluation, and prognosis prediction, while still facing challenges such as insufficient data standardization, limited model generalization, poor interpretability, and insufficient clinical translation.

This article systematically reviews the current applications, main problems, and future directions of medical AI image technology in the precision diagnosis and treatment of head and neck tumors, noting that the technology enables precise quantification of imaging features and opens new paths for early identification, efficacy evaluation, and prognosis prediction, while still facing challenges such as insufficient data standardization, limited model generalization, poor interpretability, and insufficient clinical translation.

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

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