Evidence map›Paper›PMID 41701283›Full record

ReviewHead and neck pathology2026

Artificial Intelligence Basics for Head and Neck Pathologists: A Simple Guide.

Rong Wang, Tanya M Gibson

Abstract readReview
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

2 authors.

Rong WangDepartment of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri Kansas City, Kansas City, MO, USA. wangrong@umkc.edu.ORCID http://orcid.org/0000-0002-1607-8094
Tanya M GibsonDepartment of Oral Pathology, Radiology & Medicine, School of Dentistry, University of Missouri Kansas City, Kansas City, MO, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is reshaping pathology practice by assisting pathologists in analyzing and interpreting complex tissue and cellular imaging data. AI offers new opportunities to enhance accuracy, efficiency, and consistency in histopathological evaluation and lesion diagnosis. This perspective provides a simple guide for head and neck pathologists on the fundamentals and applications of AI. It outlines the main categories and concepts of AI, describes the basic workflow of model development, and highlights its utilization in head and neck pathology. This article also summarizes key AI terminologies with examples specific to this field, discusses the evolving role of pathologists in AI development and deployment, and underscores the challenges and considerations involved in integrating AI into routine practice. Importantly, AI should be viewed not as a substitute for human expertise but as a complemental tool to reduce interobserver variability, shorten turnaround times, and help manage the growing diagnostic workload in pathology laboratories. Algorithms are only as reliable as the data and context in which they are developed. The active involvement of pathologists remains critical to ensuring diagnostic accuracy, patient safety, and high-quality, patient-centered care in head and neck pathology.

Indexed as

Artificial IntelligenceHead and Neck NeoplasmsPathology, ClinicalHumansPathologistsArtificial IntelligenceDeep LearningHead and Neck PathologyMachine Learning

Identifiers

PMID41701283
PMCPMC12913853

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.