Multimodal Artificial Intelligence Driving Precision Diagnosis and Treatment of Otolaryngologic Diseases: Key Challenges and Future Directions

The article notes that otolaryngology–head and neck surgery involves fine anatomical structures and complex adjacent relationships, concerns important functions such as hearing, smell, voice, swallowing, and breathing, and that clinical care often requires combining otoscopy or nasopharyngolaryngoscopy, imaging, pathology, audiology, and other functional examinations with clinical data. Compared with reports focused on a single modality or a single disease, this article organizes these heterogeneous information sources within one clinical context, showing that the input basis of multimodal AI comes from combinations of multiple examinations and clinical data. This is a review-type discussion based on existing understanding of the specialty's diagnostic and therapeutic characteristics; no specific study data or sample sizes are provided.

The article holds that multimodal artificial intelligence, through cross-modal fusion of heterogeneous information, offers a new technical path for disease identification, typing and staging, risk stratification, treatment decisions, and full-course management. It extends the value of multimodal fusion from single-point identification to multiple stages including typing and staging, risk stratification, treatment decisions, and full-course management, covering otology, rhinology, laryngology, and head and neck oncology. This is a directional judgment presented through a summary of representative applications; no quantitative effects are given.

The article focuses on 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. It organizes translational barriers into these several aspects rather than discussing model performance alone, extending the discussion from the algorithmic level to data governance, collaboration mechanisms, and clinical implementation. This is a problem mapping and viewpoint synthesis; no empirical results addressing these issues are reported.

The article 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 applications. It offers three directions oriented to clinical practice in China and emphasizes specialty-physician supervision and cautious expansion of large-model applications, providing a reference framework for standardized application. This is a recommendation-type discussion; no implementation outcomes or validation data are provided.

The article is positioned to provide reference for the standardized application of multimodal AI in precision diagnosis and treatment in otolaryngology–head and neck surgery, applicable to research design and clinical collaboration discussions related to otology, rhinology, laryngology, and head and neck oncology; the proposed specialty data standards, staged multicenter validation system, and specialty-physician-supervised human–machine collaboration model are aimed at specialty teams, data governance designers, and collaboration-mechanism designers seeking to advance multimodal clinical applications.

Readers may continue to watch: how feasible specialty data standards and cross-modal alignment are in real clinical data, how model generalization under missing modalities should be evaluated, how privacy protection and multicenter collaboration mechanisms can be implemented, how interpretability and clinical evidence can be integrated with workflow, and what cautious path should be followed when advancing general and specialty large models toward multimodal clinical applications. This reading was a fast parse at the abstract level and did not include figures, tables, or reference details; to understand the specific research sources and evidence strength behind the representative applications, consulting the full text is still necessary.