Advances in the Application of Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Tumors: A Review Summary

The article systematically maps AI applications across the full chain of head and neck tumor care, covering screening and diagnosis, staging, risk and prognosis prediction, treatment assistance, and follow-up management. Relative to prior work focused on a single step or modality, it aligns mainstream paradigms such as traditional machine learning, deep learning, and multimodal models with main data modalities including endoscopic images, CT/MRI imaging, digital pathology, ultrasound, and multi-omics data, forming a full-chain perspective. This is a review-level synthesis; evidence comes from the article's organization and appraisal of the study designs it covers, and the text states that it evaluates evidence quality in light of study design.

In screening, diagnosis, and staging, AI-assisted endoscopic interpretation and imaging analysis can improve lesion recognition accuracy, reduce operator dependence, and support non-invasive prediction of occult lymph node metastasis. The article notes that some multicenter studies and human-machine comparison trials show the value of human-machine collaboration, offering a higher tier of evidence than studies reporting single-center performance alone. Based on the multicenter studies and human-machine comparison trials described in the text; overall field evidence nonetheless remains dominated by single-center retrospective studies.

In risk and prognosis prediction, AI models can integrate multi-source clinical and omics data to predict disease progression. The article frames integration of multi-source clinical and omics data as a route to predicting disease progression, distinct from prediction approaches relying on a single imaging or clinical variable. The text also notes that prospective evidence for treatment response prediction remains limited, indicating that evidence strength in this direction is not yet sufficient.

In treatment assistance, AI shows auxiliary value in radiotherapy dose optimization, intraoperative structure identification, and preoperative imaging assessment. The article positions AI explicitly as an auxiliary role within the treatment workflow rather than a replacement for clinical decision-making. This is a synthesis of existing application progress; the text does not provide specific quantitative effects.

The article is intended for researchers and clinical readers interested in head and neck tumor care and medical AI applications, and is suited to mapping AI's role in screening and diagnosis, staging, prognosis prediction, and treatment assistance; its conclusions rest on the evidence tiers of the studies reviewed, and it is especially useful for identifying which steps already have multicenter or human-machine comparison evidence and which remain characterized by limited prospective evidence.

Readers may still watch for: when prospective evidence for treatment response prediction will be supplemented; progress in addressing insufficient multicenter data standardization and limited model interpretability; and whether key technologies such as explainable AI and federated learning can advance prospective research through multidisciplinary collaboration, thereby supporting standardized implementation and clinical translation. In addition, this reading is a fast parse at the abstract level and does not include figures or specific study data; assessing effect sizes and methodological details of individual studies should rely on the original text.