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Updated: May 26, 2026
Translationally-Relevant Tumor Resection Model for Murine Preclinical Models of Oral Squamous Cell Carcinoma
Published on: April 3, 2026
Digital Pathology in Head and Neck Squamous Cell Carcinoma: Translational Advances and Clinical Integration for
Sholem Hack1, Daor Hayu2, Jacob E Karni3
1City St. George's University London School of Medicine, Program Delivered by University of Nicosia at the Chaim Sheba Medical Center, Ramat Gan, Israel. sholemhack1@gmail.com.
Purpose:
Artificial intelligence (AI)-enabled digital pathology has advanced rapidly in head and neck squamous cell carcinoma (HNSCC), but its readiness for clinical implementation remains uncertain. This review evaluates the translational maturity of AI-based digital pathology applications in HNSCC and their proximity to routine clinical use.
Methods:
A structured literature search of PubMed and Scopus identified studies published between January 2020 and March 2026. Studies were included if they applied AI or machine learning to histopathologic or whole-slide images in HNSCC or related oral malignancies and reported diagnostic, prognostic, biomarker, or treatment-response outcomes. Given the narrative scope of this review, representative studies aligned with key clinical domains were selected for qualitative synthesis. A six-stage translational maturity framework was used to categorize applications based on development stage, validation, and clinical integration.
Results:
Of 444 identified records, 358 unique studies were screened, from which representative HNSCC-specific AI digital pathology studies were selected for detailed analysis. Most applications-including diagnostic classification, dysplasia grading, prognostic modeling, and therapy-response prediction-remain at early translational stages (Stage 0-1), typically limited to retrospective, single-center cohorts with minimal external validation. Biomarker quantification and HPV prediction show relatively greater maturity, with some studies approaching Stage 2, reflecting external validation and early prospective evaluation. However, calibration, decision-curve analysis, and outcome-linked endpoints are rarely reported. No HNSCC-specific AI pathology tools have demonstrated outcome-linked deployment or achieved regulatory-grade implementation, and evidence for workflow integration, clinical decision impact, and patient-centered outcomes remains limited.
Conclusion:
AI-enabled digital pathology in HNSCC remains an early-stage field. Despite promising technical performance, most applications lack the prospective validation, workflow integration, and outcome-based evidence required for clinical adoption. Progress will depend on multi-institutional prospective studies, standardized reporting of clinical utility, and demonstration that AI-assisted decisions improve patient management and outcomes.
