Evidence map›Paper›PMID 42183917›Full record

ReviewHead and neck pathology2026

Digital Pathology in Head and Neck Squamous Cell Carcinoma: Translational Advances and Clinical Integration for Pathologists, Oncologists, and Surgeons.

Sholem Hack, Daor Hayu, Jacob E Karni, Eric Remer, Eran E Alon, David Z Allen, Jo-Lawrence Bigcas, Liron Pantanowitz, Ron J Karni

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Head and neck pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sholem HackCity 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.ORCID http://orcid.org/0009-0001-0651-6994
Daor Hayu1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Jacob E KarniWashington University in St. Louis, St. Louis, MO, USA.
Eric RemerDepartment of Otolaryngology, Sheba Medical Center, Ramat Gan, Israel.
Eran E AlonDepartment of Otolaryngology, Sheba Medical Center, Ramat Gan, Israel.
David Z AllenDivision of Endocrine Head and Neck Surgery, Department of Otolaryngology, Head and Neck Surgery, Emory University, Atlanta, GA, USA.
Jo-Lawrence BigcasDepartment of Otorhinolaryngology-Head and Neck Surgery, University of California San Diego, La Jolla, CA, USA.
Liron PantanowitzDepartment of Pathology, University of Pittsburgh, Pittsburgh, USA.
Ron J KarniDepartment of Otorhinolaryngology-Head and Neck Surgery, McGovern Medical School at the University of Texas Health Science Center, 6431 Fannin Street, MSB 5.036, Houston, TX, 77030, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeArtificial 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.

methodsA 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.

resultsOf 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.

conclusionAI-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.

Indexed as

Artificial IntelligenceHead and Neck NeoplasmsSquamous Cell Carcinoma of Head and NeckHumansTranslational Research, BiomedicalArtificial intelligenceBiomarker quantificationComputational pathologyDigital pathologyHead and neck squamous cell carcinomaTranslational research

Identifiers

PMID42183917
PMCPMC13201671

What OpenQuestion holds

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Registered trials

None linked

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.