ReviewJB & JS open access
Generative Artificial Intelligence in Hip and Knee Arthroplasty: A Systematic Review of Emerging Clinical Applications in Patient Communication and Education, Documentation, and Decision Support.
Review in JB & JS open access. 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
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
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: Generative artificial intelligence (AI), including large language models (LLMs), has been increasingly explored in orthopedic surgery; however, its application within total hip and knee arthroplasty (THA/TKA) has not been clearly characterized. Therefore, we performed a systematic review to further evaluate generative AI in THA/TKA across 3 clinical domains. Methods: A PubMed and Embase systematic literature review was performed on July 9, 2025, in accordance with the Preffered Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Included studies evaluated generative AI use in THA/TKA and addressed 1 of our 3 domains. Excluded studies used nongenerative AI, involved populations not undergoing THA/TKA, or were non-English or non-peer-reviewed. Quality metrics that were assessed included blinded clinician ratings, readability scores, DISCERN scores, and diagnostic accuracy measures. The heterogeneity of the included studies led to a narrative synthesis, and no formal risk of bias was conducted. Results: Of the 91 articles retrieved, 23 met the inclusion criteria. ChatGPT versions 3.5 or 4 were assessed across all studies, and 3 studies included Google Gemini, Claude 3 Opus, and DeepSeek. Among studies in patient communication and education (n = 19), blinded clinician ratings showed that ChatGPT-generated responses to frequently asked questions (FAQ's) were as accurate, clear, and complete as surgeon-written responses. Regarding documentation (n = 2), LLMs demonstrated 97.5% to 100% accuracy in identifying operative report information and created patient consent documents with improved readability scores (grade level 12.6 vs. 16.8) and completeness (2.4/3 vs. 1.8/3). Among studies assessing decision support (n = 2), ChatGPT demonstrated high sensitivity when predicting surgical candidacy but low specificity when predicting surgical outcomes. Conclusion: LLMs performed well in several aspects of patient communication and education with support from high clinician ratings, accuracy, and readability scores. However, evidence for documentation and decision support remained limited. Moreover, proper implementation that considers clinical workflows could increase clinician efficiency and positively affect patient experience.
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.