Evidence map›Paper›PMID 41506794›Full record

ArticleClinical medicine & research2025

Artificial Intelligence in the Diagnosis, Treatment, and Prognosis of Hypopharyngeal Carcinoma: A Scoping Review.

Yuling Zhang

Abstract readScoping Review
In one paragraph

Article in Clinical medicine & research, 2025. 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

1 author.

Yuling ZhangDepartment of Otolaryngology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science, Beijing, China. zyltyu@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypopharyngeal carcinoma (HPC) has one of the poorest prognoses among all types of head and neck squamous cell carcinoma (HNSCC). Artificial intelligence (AI) is a scientific field that is in the spotlight, especially in the last decade, and AI has also been widely used in the research field of HPC. This scoping review aimed to describe the improvement of HPC clinical cares brought by AI. Literatures utilizing AI and machine learning in HPC were searched in PubMed, EMBASE, and Web of Science, and 116 articles from 1987 to 2024 were retrieved. After removing duplicate and irrelevant articles, 85 were further selected for detailed review. AI helps analyze large amounts of data from HPC patients and develop models to facilitate clinical practice. The emergence of AI improves the endoscopic, radiologic, and pathologic diagnosis accuracy of HPC and guides personalized treatment and prognosis prediction. However, there are certain unmet challenges that need to be further elucidated, like interpreting the AI algorithms into features that can be observed by humans and promoting the AI models in larger and multi-centered cohorts.

Indexed as

Artificial IntelligenceHypopharyngeal NeoplasmsSquamous Cell Carcinoma of Head and NeckHumansMachine LearningPrognosisArtificial intelligenceDiagnosisHypopharyngeal carcinomaMachine learningRobotic surgeryTherapy

Identifiers

PMID41506794
PMCPMC12782118

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.