ReviewUrolithiasis2025
What is the role of large language models in the management of urolithiasis?: a review.
Review in Urolithiasis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Enhancing AI-enabled LLMs in urolithiasis and urology: from ChatGPT through deepseek to fusion and collaboration LLMs.Urolithiasis · 2025Article
- Global Research Landscape of Artificial Intelligence in Urology: A Systematic Analysis of Emerging Trends, Clinical Impact, and Collaborative Networks (1971-2024).Medical journal of the Islamic Republic of Iran · 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
This review aimed to investigate the role of large language models (LLMs) in clinical decision support, patient counseling, and patient education in the management of urolithiasis. Eleven eligible studies were assessed following a comprehensive search of the Scopus and Web of Science databases. In the realm of clinical decision support, large language models (LLMs), particularly ChatGPT-4, have shown efficacy in areas such as the diagnosis of urolithiasis and initial treatment planning. An evaluation of the models' adherence to the European Association of Urology guidelines revealed that ChatGPT and Perplexity outperformed Bard. For patient counseling, Bing AI exhibited a robust capacity to deliver resource-based information. The studies have yielded conflicting results regarding ChatGPT versions' ability to empathize. In the context of patient education, models such as Claude-3 and ChatGPT-4 have demonstrated the capability to provide accurate and comprehensible answers to patient questions. However, it was noted that the quality of information is occasionally conveyed using complex language. LLMs have considerable potential as assistive tools in the management of urolithiasis; however, their limitations necessitate expert supervision, especially in complex cases. In the future, it is anticipated that these limitations will be mitigated through improved training and integration of these models into clinical practice. Consequently, LLMs should be employed as auxiliary tools rather than primary instruments in clinical decision-making.
Indexed as
Identifiers
40372452What 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.