ArticleHead and neck pathology2026
Spatial transcriptomics and artificial intelligence: a scoping review of emerging applications in head and neck pathology.
Article in Head and neck pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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Who cites it
2 citing papers in PubMed.
- Immunothrombotic Cell-Cell Communication Networks in Coronary Atherosclerosis: Critical Insights from Single-Cell and Spatial Systems Biology.International journal of molecular sciences · 2026Review
- Molecular and microenvironmental drivers of malignant transformation from oral potentially malignant disorders to oral squamous cell carcinoma.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSingle cell spatially resolved transcriptomics (ST) has revolutionized molecular profiling by providing the visualization of gene expression within its native tissue architecture, enabling insights into cellular heterogeneity, tumor microenvironment (TME) composition, and the molecular pathways driving disease progression. At the same time, advances in artificial intelligence (AI)-driven workflows have demonstrated significant applications within the medical field and are expected to transform the way complex diagnostic and prognostic challenges are approached. In addition, integrative analyses of spatial, histological and molecular data, offer new opportunities to uncover driver genes, identify new immunohistochemical biomarkers, and inform personalized treatment strategies, ultimately contributing to enhanced clinical decision-making and improved patient outcomes.
aimThis scoping review aims to examine recent research leveraging AI in ST to study head and neck (H&N) pathology and highlight future applications of these technologies for improving the diagnosis, risk stratification, and malignant transformation prediction. MATERIALS AND
methodsScoping literature review was conducted in accordance with PRISMA guidelines using seven electronic databases, including PubMed, Embase, Cochrane Library, IEEE Xplore, EBSCOhost, Springer, and Google Scholar. Database-specific search strategies and manual reference screening were applied to identify relevant studies published between January 2014 and May 2025.
resultsTen relevant studies were included in this review after removal of duplicates and exclusion of irrelevant articles due to incompatible formats, lack of spatial transcriptomics data, not including head and neck human tissue, or unavailable full-text access.
conclusionThis review identifies a substantial gap in the application of ST and AI within H&N pathology. Future research should focus on developing multimodal, AI-driven frameworks that integrate histopathology, spatial gene expression, and clinical metadata to improve early detection, risk stratification, and clinical decision-making in the management of OPMDs. Broader adoption of these approaches is essential to advance translational research and improve patient outcomes.
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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.