ArticleArthroplasty today2026
Evaluating the Utility of Artificial Intelligence in Conducting Systematic Reviews.
Article in Arthroplasty today, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Systematic reviews and meta-analyses represent the highest level of evidence in clinical research, but the process of article retrieval and screening is labor-intensive. Large language models, such as ChatGPT-5, may offer an efficient alternative, yet their performance in full systematic review workflows remains untested. This study compares ChatGPT-5's Deep Research and Agent Modes with human researchers in replicating gold standard systematic reviews in total joint arthroplasty. Methods: Five published systematic reviews were selected as reference articles. Three groups: orthopaedic research fellows, ChatGPT-5 Deep Research Mode, and ChatGPT-5 Agent Mode, independently identified eligible articles using standardized search terms and inclusion/exclusion criteria. Artificial intelligence (AI) searches were repeated 3 times for reproducibility. Extracted articles were evaluated against the gold standard for recall, precision, false positives/negatives, and time efficiency. Newly identified eligible studies were also assessed. Results: The research fellows dedicated 268 hours to screening 9101 articles, achieving 85.2% recall of gold standard articles. Deep Research and Agent Modes averaged 12-14 minutes per search, identifying 47.5% and 40.9% of gold standard articles, respectively. Fellows had fewer false negatives (n = 5) compared with Deep Research (n = 19) and Agent Mode (n = 12). AI models retrieved several additional eligible studies not captured by humans, demonstrating complementary potential. Conclusions: Human reviewers remain superior to current AI models in replicating systematic review article selection, particularly for nuanced inclusion/exclusion criteria. However, ChatGPT-5 significantly reduces search time and can identify additional relevant studies, suggesting its role as a valuable adjunct in systematic review workflows with expert oversight.
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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.