ReviewInternational journal of general medicine2026
Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.
Review in International journal of general medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
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Abstract
Head and neck squamous cell carcinoma (HNSCC) remains a significant global health challenge. Early detection, accurate diagnosis, and individualized treatment planning are essential for improving patient outcomes and enhancing quality of life. Artificial intelligence (AI), including radiomics and deep learning, has shown substantial potential in the early screening, accurate diagnosis, and treatment response prediction of HNSCC. In this review, we summarize the commonly used types of medical imaging and outline the basic workflows of radiomics and deep learning in medical image analysis. We then review the applications of AI in the clinical diagnosis and treatment of HNSCC across various aspects, including screening, diagnosis, staging and grading, pre-treatment evaluation, and prognostic prediction. We further discuss the convergence of AI with emerging imaging modalities, including hyperspectral imaging, optical coherence tomography, and Fourier transform infrared spectroscopy. Notably, most studies included in this review are retrospective and single-center in design, with limited external validation, underscoring the urgent need for prospective, multicenter research to facilitate clinical translation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.