Assistive, not autonomous: Generative artificial intelligence in head and neck cancer care - A scoping review

Objectives To synthesize current evidence on the clinical applications of generative artificial intelligence (GenAI), particularly large language models (LLMs), in head and neck oncology, with a focus on translational readiness, clinical safety, and real-world applicability. Methods A scoping review was conducted using structured searches of PubMed and Scopus for studies published between January 1, 2020, and December 15, 2025. Search strategies combined controlled vocabulary and free-text terms related to generative AI and head and neck oncology. Eligible studies evaluated GenAI/LLMs in tasks including TNM staging, treatment planning, tumor board support, and patient education. Non-GenAI and non-oncologic studies were excluded. Following duplicate removal, records underwent title and abstract screening with full-text review of potentially relevant studies. Due to heterogeneity in study design, outcomes, and reporting, findings were synthesized qualitatively. Results Evidence remains early-stage and heterogeneous, dominated by simulation-based and small cohort studies with limited real-world validation. GenAI performs best in structured, language-based tasks such as clinical documentation, case summarization, and patient education. Moderate agreement with clinical standards is reported for TNM staging and guideline navigation in common scenarios, with reduced reliability in complex cases. In tumor board settings, GenAI supports summarization but produces variable treatment recommendations. Patient-facing outputs are generally readable but may lack accuracy or completeness. Common limitations include hallucination, omission of key clinical factors, and overgeneralization. Conclusion GenAI shows promise as an assistive tool in head and neck oncology but is not yet suitable for autonomous clinical decision-making. Prospective, workflow-integrated evaluation and standardized validation are needed before safe clinical adoption.