Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Aims: The integration of artificial intelligence (AI) into functional urology management must be assessed for its clinical utility, but hopefully will change, perhaps to revolutionize the way LUTD and other conditions are assessed, the aim being to offer patients more rapid and effective management which enhances patient outcomes. The aim of this proposal, discussed at the ICI-RS annual meeting, is to evaluate the available evidence on AI and the way it might change the approach to urodynamic (UDS) diagnoses, including overactive bladder syndrome (OAB), and perhaps other LUTDs such as bladder outflow obstruction.
Methods: A compendium of discussion based on the current evidence related to AI and its potential applications in UDS and OAB.
Results: AI-powered diagnostic tools are being developed to analyze complex datasets from urodynamic studies, imaging, and other diagnostic tests. AI systems can leverage large volumes of clinical data to recommend personalized treatment plans based on individual patient profiles to optimize surgical procedures, enhance diagnostic precision, tailor the therapy, reduce the risk of complications, and improve outcomes. In the future, AI will be able to provide tailored counseling regarding the outcomes and potential side effects of drugs and procedures to a given patient.
Conclusion: AI's role in functional urology has been poorly investigated, and its implementation across several areas may improve clinical care and the pathophysiological understanding of functional urologic conditions.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/nau.25606 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Enhanced Giant Ferroelectric Tunneling Electroresistance in 2D Ruddlesden-Popper Oxides.
ACS Nano
September 2025
Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, and School of Physics and Technology, Wuhan University, Wuhan 430072, China.
Ferroelectric tunnel junctions (FTJs) based on ferroelectric switching and quantum tunneling effects with thickness down to a few unit cells have been explored for applications of two-dimensional (2D) electronic devices in data storage and neural networks. As a key performance indicator, the enhanced tunneling electrosistance (TER) ratio provides a broader dynamic range for precise modulation of synaptic weights, improving the stability and accuracy of neural networks. Herein, we report an observation of pronounced enhancement in the TER ratio by over 4 orders of magnitude through the fabrication of large-scale heterostructures combining bismuth ferrite with two-dimensional Ruddlesden-Popper oxide BiFeO.
View Article and Find Full Text PDFHollow polydopamine for the enhanced ultrasensitive dual-modal immunochromatographic detection of Salmonella typhimurium in dairy products via machine learning.
Mikrochim Acta
September 2025
College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling, 712100, Shaanxi, China.
Salmonella typhimurium (S. typhimurium) A dual-mode colorimetric/photothermal immunochromatographic strip (ICS) employing hollow polydopamine nanoparticles (h-PDA) is reported for the ultrasensitive detection of Salmonella typhimurium (S. typhimurium).
View Article and Find Full Text PDFMultiple large language models versus clinical guidelines for postmenopausal osteoporosis: a comparative study of ChatGPT-3.5, ChatGPT-4.0, ChatGPT-4o, Google Gemini, Google Gemini Advanced, and Microsoft Copilot.
Arch Osteoporos
September 2025
Department of Family Medicine, Chang-Gung Memorial Hospital, Linkou Branch, Taoyuan City, Taiwan.
Unlabelled: The study assesses the performance of AI models in evaluating postmenopausal osteoporosis. We found that ChatGPT-4o produced the most appropriate responses, highlighting the potential of AI to enhance clinical decision-making and improve patient care in osteoporosis management.
Purpose: The rise of artificial intelligence (AI) offers the potential for assisting clinical decisions.
Assessing the ability of large language models to simplify lumbar spine imaging reports into patient-facing text: a pilot study of GPT-4.
Skeletal Radiol
September 2025
Department of Orthopaedic Surgery, Northwestern University, Chicago, IL, USA.
Objective: To assess the ability of large language models (LLMs) to accurately simplify lumbar spine magnetic resonance imaging (MRI) reports.
Materials And Methods: Patients who underwent lumbar decompression and/or fusion surgery in 2022 at one tertiary academic medical center were queried using appropriate CPT codes. We then identified all patients with a preoperative ICD diagnosis of lumbar spondylolisthesis and extracted the latest preoperative spine MRI radiology report text.
Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.
Eur Radiol
September 2025
Quantitative Imaging Biomarkers in Medicine, Quibim, Valencia, Spain.
Objectives: In non-small cell lung cancer (NSCLC), non-invasive alternatives to biopsy-dependent driver mutation analysis are needed. We reviewed the effectiveness of radiomics alone or with clinical data and assessed the performance of artificial intelligence (AI) models in predicting oncogene mutation status.
Materials And Methods: A PRISMA-compliant literature review for studies predicting oncogene mutation status in NSCLC patients using radiomics was conducted by a multidisciplinary team.