ArticlePaediatrics & child health2025
Will ChatGPT-4 improve the quality of medical abstracts?
Article in Paediatrics & child health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Feasibility and impact of a large language model pipeline for surgical trial abstracts.NPJ digital medicine · 2026Article
- Characterizing the Increase in Artificial Intelligence Content Detection in Oncology Scientific Abstracts From 2021 to 2023.JCO clinical cancer informatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: ChatGPT received attention for medical writing. Our objective was to evaluate whether ChatGPT 4.0 could improve the quality of abstracts submitted to a medical conference by clinical researchers. Methods: This was an experimental study involving 24 international researchers (the participants) who provided one original abstract intended for submission at the 2024 Pediatric Academic Society (PAS) conference. We asked ChatGPT-4 to improve the quality of the abstract while adhering to PAS submission guidelines. Participants received the revised version and were tasked with creating a final abstract. The quality of each version (original, ChatGPT and final) was evaluated by the participants themselves using a numeric scale (0-100). Additionally, three co-investigators assessed abstracts blinded to the version. The primary analysis focused on the mean difference in scores between the final and original abstracts. Results: Abstract quality varied between the three versions with mean scores of 82, 65 and 90 for the original, ChatGPT and final versions, respectively. Overall, the final version displayed significantly improved quality compared to the original (mean difference 8.0 points; 95% CI: 5.6-10.3). Independent ratings by the co-investigators confirmed statistically significant improvements (mean difference 1.10 points; 95% CI: 0.54-1.66). Participants identified minor (n = 10) and major (n = 3) factual errors in ChatGPT's abstracts. Conclusion: ChatGPT 4.0 does not produce abstracts of better quality than the one crafted by researchers but it offers suggestions to help them improve their abstracts. It may be more useful for researchers encountering challenges in abstract generation due to limited experience or language barriers.
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.