ArticleInternational neurourology journal2024
Exploring Large Language Models and the Metaverse for Urologic Applications: Potential, Challenges, and the Path Forward.
Article in International neurourology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Can ChatGPT Be Trusted for Urologic Patient Education? A Comparative Study of Stress Urinary Incontinence-Related Information From Versions 3.5 and 4.International urogynecology journal · 2026Article
- Transforming Urological Care With Artificial Intelligence Chatbots: From Digital Assistants to Clinical Partners.International neurourology journal · 2026Review
- From Machine Learning to Generative Artificial Intelligence in Urology: Technological Evolution and Future Perspectives.International neurourology journal · 2026Review
- LLM-enabled adaptive scheduling in IoT sensing for optimized network performance.Scientific reports · 2026Article
- Privacy-by-Design Framework for Large Language Model Chatbots in Urology.International neurourology journal · 2025Article
- Are ChatGPT, My AI Snapchat, and Metaverse used by dental students as reliable sources of dental education?Frontiers in dental medicine · 2025Article
- The Evolving Landscape of Urology.International neurourology journal · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The metaverse, a 3-dimensional digital platform that enables users to interact and engage in realistic virtual activities beyond time and space limitations, has garnered significant investment across industries, particularly in healthcare. In the medical field, the metaverse shows promise as a digital therapeutic platform to enhance interaction between medical professionals and patients. Concurrently, generative artificial intelligence, especially large language models, is being integrated into healthcare for applications in data analysis, image recognition, and natural language processing. In urology, large language models (LLMs) support are increasingly used in urology for tasks such as image diagnosis, data processing, patient education, and treatment assistance in order to provide significant support in clinical settings. By combining LLMs with the immersive capabilities of the metaverse, new possibilities emerge to improve urologic treatment in areas that require consistent treatments, habit formation, and long-term management. This paper reviews current research and applications of LLMs in urology, discusses the challenges associated with their use including data quality, bias, security, and ethical issues, and explores the need for regulatory standards. Furthermore, it highlights the potential of a metaverse-based digital platform to improve urologic care and streamline information exchange to maximize the benefits of this integrated approach in future healthcare applications.
Indexed as
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.