Evidence map›Paper›PMID 39638453›Full record

ArticleInternational neurourology journal2024

Exploring Large Language Models and the Metaverse for Urologic Applications: Potential, Challenges, and the Path Forward.

Hyung Jun Park, Eun Joung Kim, Jung Yoon Kim

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. The Evolving Landscape of Urology.International neurourology journal · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hyung Jun ParkDepartment of Game and Interactive Media, Graduate School of Game, Gachon University, Seongnam, Korea.
Eun Joung KimDepartment of Game Contents, College of Smart Content, Kyungil University, Gyeongsan, Korea.
Jung Yoon KimDepartment of Game Media, College of IT Convergence, Gachon University, Seongnam, Korea.

Funding

Korea Creative Content AgencyMinistry of Culture, Sports and Tourism RS-2023-00227648Ministry of EducationNational Research Foundation of Korea 2022R1F1A1066602
6 · The paper itself

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

Artificial intelligenceDigital therapeuticsLarge language modelsMetaverseUrology

Identifiers

PMID39638453
PMCPMC11627225

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.