ArticleJMIR cancer2025
Using ChatGPT to Improve the Presentation of Plain Language Summaries of Cochrane Systematic Reviews About Oncology Interventions: Cross-Sectional Study.
Article in JMIR cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07140107 (Patients' Perceptions and Comprehension of Original Versus AI-Generated Plain Language Summaries of Cochrane Oncology Systematic Reviews), which is not on this map. Cited by 6 papers.
What it found
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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.
Patients' Perceptions and Comprehension of Original Versus AI-Generated Plain Language Summaries of Cochrane Oncology Systematic Reviews
Who cites it
6 citing papers in PubMed.
- Large Language Model Simplification of Open Access Pediatric Strabismus Literature: Cross-Sectional Validation of Readability and Clinical Fidelity.JMIR formative research · 2026Article
- Characterizing HIV-related patient portal preferences among men who have sex with men in the United States.BMC public health · 2026Article
- Auditing frontier general-purpose large language models in biomedical tasks: reasoning gains, extraction limits, and benchmark reliability.Research square · 2026Article
- Using ChatGPT-4 for Lay Summarization in Prostate Cancer Research to Advance Patient-Centered Communication: Large-Scale Generative AI Performance Evaluation.Journal of medical Internet research · 2025Article
- Editor's view: What makes science successful?Journal of global health · 2025Article
- Using ChatGPT to Improve the Presentation of Plain Language Summaries of Cochrane Systematic Reviews About Oncology Interventions: Cross-Sectional Study.JMIR cancer · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Plain language summaries (PLSs) of Cochrane systematic reviews are a simple format for presenting medical information to the lay public. This is particularly important in oncology, where patients have a more active role in decision-making. However, current PLS formats often exceed the readability requirements for the general population. There is still a lack of cost-effective and more automated solutions to this problem. Objective: This study assessed whether a large language model (eg, ChatGPT) can improve the readability and linguistic characteristics of Cochrane PLSs about oncology interventions, without changing evidence synthesis conclusions. Methods: The dataset included 275 scientific abstracts and corresponding PLSs of Cochrane systematic reviews about oncology interventions. ChatGPT-4 was tasked to make each scientific abstract into a PLS using 3 prompts as follows: (1) rewrite this scientific abstract into a PLS to achieve a Simple Measure of Gobbledygook (SMOG) index of 6, (2) rewrite the PLS from prompt 1 so it is more emotional, and (3) rewrite this scientific abstract so it is easier to read and more appropriate for the lay audience. ChatGPT-generated PLSs were analyzed for word count, level of readability (SMOG index), and linguistic characteristics using Linguistic Inquiry and Word Count (LIWC) software and compared with the original PLSs. Two independent assessors reviewed the conclusiveness categories of ChatGPT-generated PLSs and compared them with original abstracts to evaluate consistency. The conclusion of each abstract about the efficacy and safety of the intervention was categorized as conclusive (positive/negative/equal), inconclusive, or unclear. Group comparisons were conducted using the Friedman nonparametric test. Results: ChatGPT-generated PLSs using the first prompt (SMOG index 6) were the shortest and easiest to read, with a median SMOG score of 8.2 (95% CI 8-8.4), compared with the original PLSs (median SMOG score 13.1, 95% CI 12.9-13.4). These PLSs had a median word count of 240 (95% CI 232-248) compared with the original PLSs' median word count of 364 (95% CI 339-388). The second prompt (emotional tone) generated PLSs with a median SMOG score of 11.4 (95% CI 11.1-12), again lower than the original PLSs. PLSs produced with the third prompt (write simpler and easier) had a median SMOG score of 8.7 (95% CI 8.4-8.8). ChatGPT-generated PLSs across all prompts demonstrated reduced analytical tone and increased authenticity, clout, and emotional tone compared with the original PLSs. Importantly, the conclusiveness categorization of the original abstracts was unchanged in the ChatGPT-generated PLSs. Conclusions: ChatGPT can be a valuable tool in simplifying PLSs as medically related formats for lay audiences. More research is needed, including oversight mechanisms to ensure that the information is accurate, reliable, and culturally relevant for different audiences.
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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.