ArticleJAMIA open2026
Capability of chatbots powered by large language models to support the screening process of scoping reviews: a feasibility study.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Compact large language models for title and abstract screening in systematic reviews: An assessment of feasibility, accuracy, and workload reduction.Research synthesis methods · 2026Article
- Harnessing ChatGPT for abstract screening in health-related scoping reviews: the role of structured eligibility criteria.BMC health services research · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The surge in publications increases screening time required to maintain high-quality literature reviews. One of the most time-consuming phases is title and abstract screening. Machine learning tools have semi-automated this process for systematic reviews, with limited success for scoping reviews. ChatGPT, a chatbot based on a large language model, might support scoping review screening by identifying key concepts and themes. We hypothesize that ChatGPT outperforms the semi-automated tool Rayyan, increasing efficiency at acceptable costs while maintaining a low type II error. Materials and Methods: We conducted a retrospective study using human screening decisions on a scoping review of 15 307 abstracts as a benchmark. A training set of 100 abstracts was used for prompt engineering for ChatGPT and training Rayyan. Screening decisions for all abstracts were obtained via an application programming interface for ChatGPT and manually for Rayyan. We calculated performance metrics, including accuracy, sensitivity, and specificity with Stata. Results: ChatGPT 4.0 decided upon 15 306 abstracts, vastly outperforming Rayyan. ChatGPT 4.0 demonstrated an accuracy of 68%, specificity of 67%, sensitivity of 88%-89%, a negative predictive value of 99%, and an 11% false negative rate when compared to human researchers' decisions. The workload savings were at 64% reasonable costs. Discussion and Conclusion: This study demonstrated ChatGPT's potential to be applied in the first phase of the literature appraisal process for scoping reviews. However, human oversight remains paramount. Additional research on ChatGPT's parameters, the prompts and screening scenarios is necessary in order to validate these results and to develop a standardized approach.
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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.