Evidence map›Paper›PMID 41480197›Full record

ArticleWorld journal of gastrointestinal oncology2025

Cost

Sunny Chi Lik Au

Abstract readEditorial
In one paragraph

Article in World journal of gastrointestinal oncology, 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.

Sunny Chi Lik AuSchool of Clinical Medicine, The University of Hong Kong, Hong Kong 999077, China. kilihcua@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As large language models increasingly permeate medical workflows, a recent study evaluating ChatGPT 4.0's performance in addressing patient queries about endoscopic submucosal dissection and endoscopic mucosal resection offers critical insights into three domains: Performance parity, cost democratization, and clinical readiness. The findings highlight ChatGPT's high accuracy, completeness, and comprehensibility, suggesting potential cost efficiency in patient education. Yet, cost-effectiveness alone does not ensure clinical utility. Notably, the study relied exclusively on text-based prompts, omitting multimodal data such as photographs or endoscopic scans. This is a significant limitation in a visually driven field like endoscopy, where large language model performance may drop precipitously without image context. Without multimodal integration, artificial intelligence tools risk failing to capture key diagnostic signals, underscoring the need for cautious adoption and robust validation in clinical practice.

Indexed as

Artificial intelligenceChatGPTCostEndoscopic mucosal resectionEndoscopic submucosal dissectionLarge language modelsPatient education

Identifiers

PMID41480197
PMCPMC12754347

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.