Artificial intelligence (AI) is reshaping the anatomical and
molecular mapping of head and neck cancers by integrating immunological,
microbiome, and structural biomarkers into cohesive diagnostic and prognostic
frameworks. Advances in deep learning, digital pathology, radiomics, and
spatial omics now allow automated segmentation, immune landscape
characterisation, and multi-omics integration with high accuracy and
reproducibility. AI-driven spatial profiling has enhanced understanding of
tumour–immune interactions, while automated imaging analysis has refined
structural biomarker discovery, supporting improved radiotherapy planning and
outcome prediction. Concurrently, microbiome-focused machine learning
approaches are revealing microbial signatures linked to immunotherapy response.
Despite these advances, limitations remain, including small datasets, a lack of
standardised protocols, and challenges in model interpretability. However,
current literature remains fragmented, often evaluating immunological, microbial,
or imaging biomarkers in isolation rather than through integrated AI
frameworks. By bridging this gap, AI mapping can facilitate earlier diagnosis,
personalise therapy selection, and improve survival outcomes in clinical
practice. This review highlights the transformative role of AI in biomarker
discovery and precision oncology for head and neck cancers, emphasising the
need for multicenter validation, explainable AI, and harmonised methodologies
to enable clinical translation.
Author(s) Details
Shrikrishna B H
Department of ENT, All India Institute of Medical Sciences, Bibinagar,
India.
Deepa G
Department of Anatomy, All India Institute of Medical Sciences, Bibinagar,
India.
Please see the book here :- https://doi.org/10.9734/bpi/mbrao/v5/6215
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