SynthesisJournal of medical systems2025
How Well Do ChatGPT and Claude Perform in Study Selection for Systematic Review in Obstetrics.
Synthesis in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 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
- Psychodermatology: Current Scope and Future Prospects.Indian dermatology online journal · 2026Article
- Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges.Methods and protocols · 2026Article
- From digital assistants to clinical partners: revolutionizing pediatric urology through large language model-powered decision support and patient education.World journal of urology · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of generative AI in systematic review workflows has gained attention for enhancing study selection efficiency. However, evidence on its screening performance remains inconclusive, and direct comparisons between different generative AI models are still limited. The objective of this study is to evaluate the performance of ChatGPT-4o and Claude 3.5 Sonnet in the study selection process of a systematic review in obstetrics. A literature search was conducted using PubMed, EMBASE, Cochrane CENTRAL, and EBSCO Open Dissertations from inception till February 2024. Titles and abstracts were screened using a structured prompt-based approach, comparing decisions by ChatGPT, Claude and junior researchers with decisions by an experienced researcher serving as the reference standard. For the full-text review, short and long prompt strategies were applied. We reported title/abstract screening and full-text review performances using accuracy, sensitivity (recall), precision, F1-score, and negative predictive value. In the title/abstract screening phase, human researchers demonstrated the highest accuracy (0.9593), followed by Claude (0.9448) and ChatGPT (0.9138). The F1-score was the highest among human researchers (0.3853), followed by Claude (0.3724) and ChatGPT (0.2755). Negative predictive value (NPV) was high across all screeners: ChatGPT (0.9959), Claude (0.9961), and human researchers (0.9924). In the full-text screening phase, ChatGPT with a short prompt achieved the highest accuracy (0.904), highest F1-score (0.90), and NPV of 1.00, surpassing the performance of Claude and human researchers. Generative AI models perform close to human levels in study selection, as evidenced in obstetrics. Further research should explore their integration into evidence synthesis across different fields.
Indexed as
Identifiers
40906005What Socratic 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 Socratic graph.