Evidence map›Paper›PMID 41127402›Full record

ArticleFrontiers in medicine2025

Assessing the ability of ChatGPT 4.0 in generating check-up reports.

Yikai Chen, Yuxin Liu, Yuanchang Huang, Xiujie Huang, Zhuoqun Zheng, Fangjie Yang, Haiming Lin, Haoyu Lin, Xinxin Li, Aosi Xie and 1 more

Abstract read
In one paragraph

Article in Frontiers in medicine, 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

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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

11 authors.

Yikai Chen *Department of Gastroenterological Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Yuxin Liu *Health Care Center, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Yuanchang Huang *Department of Gastroenterological Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Xiujie HuangDepartment of Gastroenterological Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Zhuoqun ZhengDepartment of Gastroenterological Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Fangjie YangDepartment of Gastroenterological Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Haiming LinDepartment of Orthopaedics, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Haoyu LinDepartment of Thyroid and Breast Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Xinxin LiDepartment of Gastroenterological Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Aosi XieDepartment of Gastroenterological Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Yiteng HuangDepartment of Gastroenterological Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: ChatGPT (Chat Generative Pre-trained Transformer), a generative language model, has been applied across various clinical domains. Health check-ups, a widely adopted method for comprehensively assessing personal health, are now chosen by an increasing number of individuals. This study aimed to evaluate ChatGPT 4.0's ability to efficiently provide patients with accurate and personalized health reports. Methods: A total of 89 check-up reports generated by ChatGPT 4.0 were assessed. The reports were derived from the Check-up Center of the First Affiliated Hospital of Shantou University Medical College. Each report was translated into English by ChatGPT 4.0 and graded independently by three qualified doctors in both English and Chinese. The grading criteria encompassed six aspects: adherence to current treatment guidelines (Guide), diagnostic accuracy (Diagnosis), logical flow of information (Order), systematic presentation (System), internal consistency (Consistency), and appropriateness of recommendations (Suggestion), each scored on a 4-point scale. The complexity of the cases was categorized into three levels (LOW, MEDIUM, HIGH). Wilcoxon rank sum test and Kruskal-Wallis test were selected to examine differences in grading across languages and complexity levels. Results: ChatGPT 4.0 demonstrated strong performance in adhering to clinical guidelines, providing accurate diagnoses, systematic presentation, and maintaining consistency. However, it struggled with prioritizing high-risk items and providing comprehensive suggestions. In the "Order" category, a significant proportion of reports contained mixed data, several reports being completely incorrect. In the "Suggestion" category, most reports were deemed correct but inadequate. No significant language advantage was observed, with performance varying across complexity levels. English reports showed significant differences in grading across complexity levels, while Chinese reports exhibited distinct performance across all categories. Conclusion: In conclusion, ChatGPT 4.0 is currently well-suited as an assistant to the chief examiner, particularly for handling simpler tasks and contributing to specific sections of check-up reports. It holds the potential to enhance medical efficiency, improve the quality of clinical check-up work, and deliver patient-centered services.

Indexed as

artificial intelligenceChatGPT 4.0check-uphealth care servicehealth report

Identifiers

PMID41127402
PMCPMC12537671

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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.