ArticleDigital health
Effectiveness of ChatGPT to provide esophageal cancer information: A SERVQUAL-based analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- The development of a performance evaluation index system for internet hospitals in Qingdao, China: A Delphi consensus study.SAGE open medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: ChatGPT and other AI-driven language models are increasingly used in healthcare for disseminating medical information. However, their performance in providing accurate and empathetic responses to patients with specific diseases remains uncertain. Objective: This study aimed to evaluate the effectiveness and reliability of ChatGPT in providing esophageal cancer-related information using the SERVQUAL framework, focusing on five dimensions: Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Methods: Ten representative questions on esophageal cancer were selected through search engine analysis and specialist consultation. ChatGPT generated responses, which were evaluated by 48 gastroenterologists using a 5-point Likert scale aligned with the SERVQUAL model. Statistical analysis was conducted using R 4.4.0 to compare responses between specialist and non-specialist physicians. Results: ChatGPT performed well in providing structured, logical, and generally informative responses, particularly in the prevention domain. However, limitations were identified in its responsiveness and empathy. Significant differences were observed between specialists and non-specialists in evaluating certain answers, especially regarding reliability and cutting-edge knowledge. ChatGPT often failed to reflect the latest clinical guidelines or regional risk-specific recommendations. Conclusion: While ChatGPT shows potential in patient education for esophageal cancer, its current outputs lack clinical specificity and up-to-date medical insight. AI tools should be continuously improved with dynamic data integration and specialist supervision to ensure reliability and relevance in real-world healthcare scenarios.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.