Development of an AI-Agent for Urological Disease Diagnosis and Treatment
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Urinary Stones, Prostate Cancer, Bladder Cancer. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Development of an AI-Agent for Diagnosis and Treatment of Urological Diseases
Overview
Urological diseases such as urinary stones, prostate cancer, and bladder cancer are very common and often require highly specialized diagnosis and treatment. Today, the quality of care can vary between doctors, and there are not enough urology specialists to meet patient demand. Artificial intelligence (AI) may help doctors make faster and more consistent decisions. This study aims to develop and test an AI-powered assistant called "UroAgent" that supports doctors in diagnosing and treating urological diseases. UroAgent is built on a large language model trained specifically for urology and is connected to tools that help it retrieve medical knowledge and analyze images. To build and test UroAgent, the research team will use 1,500 past patient records from 2010-2025 and collect 500 new patient cases for validation, for a total of 2,000 cases. This is an observational study: no patient's medical treatment will be changed because of it. The goal is to create a reliable AI tool that helps improve urological care for patients.
Detailed description
This study protocol describes an observational study aiming to develop and validate UroAgent, an artificial-intelligence agent for the diagnosis and treatment of urological diseases. A total of 2,000 urological disease cases will be collected, comprising 1,500 retrospective cases recorded at the center between 2010 and 2025 for model development and 500 prospectively enrolled cases for independent performance validation. The primary evaluation is the concordance between UroAgent's diagnostic and treatment recommendations and the reference standards established by senior urologists, assessed through diagnostic accuracy, recommendation appropriateness, completeness, and safety; secondary evaluations include the agent's performance across disease subtypes (urinary stones, prostate cancer, bladder cancer) and its image-interpretation capability. All records will undergo de-identification, and the study will adhere to rigorous ethical standards and a pre-specified statistical analysis plan to provide robust evidence for the clinical application of this urology-specific AI agent.
Primary outcome measures
- Diagnostic Accuracy of UroAgent [Time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.]
Secondary outcome measures (4)
- Expert Subjective Accuracy Rating [Time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.]
- Treatment Recommendation Appropriateness of UroAgent [Time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.]
- Clinical Safety of UroAgent Recommendations [Time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.]
- Concordance and Non-Inferiority of UroAgent versus Clinician Diagnoses [Time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.]
Eligibility criteria
Inclusion criteria
- Diagnosed with a urological disease (e.g., urinary stones, prostate cancer, bladder cancer, and other urological conditions).
- Availability of complete clinical information, imaging data, and surgical video required for model development and validation.
Exclusion criteria
1\. Missing clinical information, imaging data, or surgical video.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 3 centers
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University — Guangzhou
- Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University — Shantou
- Ganzhou People's Hospital — Ganzhou
Identifiers
NCT: NCT07721935 · SYSKY-2025-151-03