322 - Role of Large Language Models in Urology: A systematic review and meta-analysis
Bibliographic record
Abstract
Hypothesis / aims of study Large Language Models (LLMs) have demonstrated transformative potential, with promising insights that could potentially enhance clinical practice. Their application in overall healthcare practices is increasingly noted, but their cumulative usage in the field of urology is less explored. Our study aimed to systematically review the current evidence on the usage of LLMs in urology and perform a quantitative analysis of comparable studies. Study design, materials and methods We searched electronic databases namely, Pubmed, Embase, Web of Science, and Scopus to include studies involving urological data or patients in which LLMs were utilized in clinical management or other relevant applications. The studies were grouped based on the application of the LLM that was being studied as: 1) Answering FAQs, 2) Patient Information Materials, 3) Clinical Practice, 4) Medical examination and 5) Other applications. The quality assessment was done utilising the Newcastle-Ottawa Scale. Results A total of 814 articles were found, and after the removal of duplicates and screening of the articles, 39 studies were included in our review. Various applications of LLMs in different domains were listed, and details regarding the training, outcomes, and limitations were studied. ChatGPT was the most commonly studied LLM. The pooled accuracy of the output of various LLMs on various medical examinations was found to be 63.24 (CI 53.69 – 72.72), and a forest plot was constructed. Studies analysing the output of LLMs in answering frequently asked questions reported an accuracy of 67.08% to 100%, with ChatGPT – 4 outperforming ChatGPT – 3.5. Interpretation of results The findings of this systematic review and meta-analysis underscore the growing role of LLMs in urology, highlighting their potential to enhance clinical practice across diverse applications. The pooled accuracy of 63.24% for LLM performance on medical examinations reflects moderate reliability, with variability likely influenced by differences in training data, model architecture, and evaluation methods. Notably, ChatGPT emerged as the most frequently studied LLM, with its latest version (ChatGPT-4) demonstrating superior accuracy (67.08% to 100%) in answering frequently asked questions compared to its predecessor, ChatGPT-3.5. This suggests that advancements in LLM iterations can significantly improve performance, particularly in patient-facing tasks. However, the results also reveal a lack of standardization in evaluation metrics and methodologies across studies, which poses challenges for consistent benchmarking and integration into clinical workflows. Concluding message LLMs' applications are diverse and have augmented urological practice. However, uniform evaluation methods and performance metrics for assessing the output generated for various purposes are needed to further streamline the use and synchronous incorporation of LLM in regular urological practice. Download: Download high-res image (104KB) Download: Download full-size image Figure 1 . Funding None Clinical Trial No Subjects None
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.107 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.036 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".