Evidence map›Paper›PMID 42243967›Full record

ReviewJournal of translational medicine2026

AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.

Yuqi Yang, Xiao Jia, Xi Wang, Pingdong Cao, Jian Zhu, Chuanxi Wang, Zhe Yang, Qiang Wen

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of translational medicine, 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

8 authors.

Yuqi YangDepartment of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, 324 Jingwu Road, Jinan, Shandong, 250021, China.
Xiao JiaSchool of Control Science and Engineering, Shandong University, Jinan, China.
Xi WangDepartment of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA.
Pingdong CaoDepartment of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, 324 Jingwu Road, Jinan, Shandong, 250021, China.
Jian ZhuDepartment of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, 250021, China.
Chuanxi WangDepartment of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, 324 Jingwu Road, Jinan, Shandong, 250021, China.
Zhe YangDepartment of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, 324 Jingwu Road, Jinan, Shandong, 250021, China.
Qiang WenDepartment of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, 324 Jingwu Road, Jinan, Shandong, 250021, China. wq890425@126.com.ORCID 0000-0001-6091-7791

Funding

Jinan Science and Technology Clinical Medicine Innovation Plan 20225011 and 20238073Natural Science Foundation of Shandong Province ZR2024MH268Shandong Provincial Medical System Staff Science and Technology Innovation Program SDYWZGKCJH2022018
6 · The paper itself

Abstract

backgroundEsophageal cancer (EC) remains one of the leading causes of cancer-related mortality worldwide. Accurate staging, treatment planning, and prognostic assessment are essential for improving clinical management and patient outcomes. In recent years, artificial intelligence (AI) approaches integrating clinicopathological, imaging, and genomic data have shown considerable potential in these areas. MAIN BODY: Over the past two years, research in this field has advanced rapidly, supported by the growing availability of large datasets and increasing adoption of multicenter external validation. Recent studies suggest that AI can improve real-time diagnosis and enhance the prediction of treatment response in patients with EC.

conclusionsThis review summarizes recent advances in AI applications for esophageal cancer, discusses current challenges, and highlights future directions for research and clinical implementation.

Indexed as

Artificial IntelligenceDigital HealthEsophageal NeoplasmsTranslational Research, BiomedicalHumansPrognosisArtificial intelligenceDeep learningEsophageal cancerMachine learningPrognosis

Identifiers

PMID42243967
PMCPMC13343953

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.