Development of an AI-Agent for Urological Disease Diagnosis and Treatment

Development of an AI-Agent for Diagnosis and Treatment of Urological Diseases

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.

Study Overview

• Status: Recruiting
• Conditions: Prostate Cancer; Bladder Cancer; Urinary Stones

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.

• Study Type: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

• Study Contact: Name: Mingzhou Dai; Phone Number: +86 19924684262; Email: daimzh3@mail2.sysu.edu.cn

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510000
      • Recruiting
      • Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
      • Contact: Mingzhou Dai; Phone Number: +86 19924684262; Email: daimzh3@mail2.sysu.edu.cn
    • Shantou, Guangdong, China, 516600
      • Recruiting
      • Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University
      • Contact: Zhuang Tang; Phone Number: +86 13666144867; Email: tangzh23@mail.sysu.edu.cn
  • Jiangxi
    • Ganzhou, Jiangxi, China, 341000
      • Recruiting
      • Ganzhou People's Hospital
      • Contact: Cong Lai; Phone Number: +86 19872753573; Email: laic@mail2.sysu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study population comprises patients diagnosed with urological diseases at Sun Yat-sen Memorial Hospital, including urinary stones, prostate cancer, bladder cancer, and other urological conditions. A total of 2,000 participants (cases) will be included: 1,500 retrospective cases with records from 2010 to 2025, and 500 prospective, consecutively enrolled cases. Both sexes are eligible, and the population is predominantly adult (the protocol does not specify an age cutoff; an age criterion of ≥18 years is recommended for registry entry, per the note in section 16). All included participants have complete clinical information, imaging data, and surgical video available for model development and validation. Records are de-identified prior to use. The population reflects the real-world case mix of a tertiary urology center.

Description

Inclusion Criteria:

1. Diagnosed with a urological disease (e.g., urinary stones, prostate cancer, bladder cancer, and other urological conditions).
2. 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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Urological Disease Cohort; The cohort consists of patients diagnosed with urological diseases-including urinary stones, prostate cancer, and bladder cancer-at Sun Yat-sen Memorial Hospital. A total of 2,000 cases are included: 1,500 retrospective cases recorded between 2010 and 2025 (used for model development) and 500 prospectively and consecutively enrolled cases (used for independent validation); all records are de-identified. The intervention (technology) of interest is "UroAgent," an artificial-intelligence diagnostic-and-treatment agent built on a urology-specialized large language model with integrated tool modules (knowledge retrieval, image interpretation). For each case, UroAgent's diagnostic and treatment recommendations are generated and compared with the analyses provided by human urology specialists, to evaluate the agent's performance-diagnostic accuracy, recommendation appropriateness, completeness, and safety-against expert judgment.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic Accuracy of UroAgent	The primary outcome is UroAgent's diagnostic accuracy, measured as the F1 score of its leading diagnosis against the reference-standard final diagnosis. The reference standard is established by senior urologists from pathology, imaging, and clinical course. F1 = 2 × Precision × Recall / (Precision + Recall), computed per case and aggregated as macro-F1 across the 2,000-case cohort (1,500 retrospective + 500 prospective). Unit of measure: F1 score (range 0-1).	Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Expert Subjective Accuracy Rating	Independent urologists rate the accuracy of UroAgent's diagnosis on a 5-point Likert scale (1 = completely inaccurate, 5 = completely accurate), blinded to model identity. Reported as the mean rating and the percentage of cases rated ≥ 4; disagreements resolved by a third urologist. Unit of measure: mean rating (1-5) and percentage of cases rated accurate (%).	Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Treatment Recommendation Appropriateness of UroAgent	The proportion of cases in which UroAgent's management recommendation is rated appropriate. Two independent urologists rate each recommendation on a 5-point Likert appropriateness scale (1 = completely inappropriate, 5 = completely appropriate), blinded; "appropriate" defined as Likert ≥ 4; disagreement resolved by a third urologist. Unit of measure: percentage of cases (%) rated appropriate.	Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Clinical Safety of UroAgent Recommendations	The rate of clinically unsafe recommendations. Each case is reviewed by senior urologists using a safety rubric and flagged (binary per case) for any contraindicated, erroneous, or potentially harmful recommendation. Unit of measure: percentage of cases (%) with ≥ 1 unsafe recommendation.	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	On the same cases, UroAgent's diagnoses are compared with those of practicing urologists. Reported as the agreement rate (%) between UroAgent and clinician diagnoses, plus the diagnostic-accuracy difference in percentage points (pp); Cohen's κ reported as a supplementary statistic. Both are assessed against the reference-standard final diagnosis. Unit of measure: agreement rate (%) and accuracy difference (pp).	Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.

Collaborators and Investigators

• Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
• Collaborators: Shenshan Medical Center, Memorial Hospital of Sun Yat-sen University; Ganzhou City People's Hospital

Study record dates

Study Major Dates

• Study Start (Actual): April 1, 2025
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): June 30, 2027

Study Registration Dates

• First Submitted: July 12, 2026
• First Submitted That Met QC Criteria: July 18, 2026
• First Posted (Actual): July 23, 2026

Study Record Updates

• Last Update Posted (Actual): July 23, 2026
• Last Update Submitted That Met QC Criteria: July 18, 2026
• Last Verified: July 1, 2026

More Information

Terms related to this study

• Keywords: Clinical Decision Support; Prostate Cancer; Artificial Intelligence; Bladder Cancer; Urological Diseases; Urinary Stones; Large Language Model; AI Agent
• Additional Relevant MeSH Terms: Urogenital Diseases; Genital Diseases; Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Prostatic Diseases; Male Urogenital Diseases; Calculi; Pathological Conditions, Anatomical; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urolithiasis; Urologic Neoplasms; Urinary Bladder Diseases; Pathological Conditions, Signs and Symptoms; Prostatic Neoplasms; Urologic Diseases; Urinary Bladder Neoplasms; Urinary Calculi
• Other Study ID Numbers: SYSKY-2025-151-03

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Individual participant data (IPD) will not be shared. This single-center observational study uses de-identified clinical records, imaging, and surgical video. The retrospective cases (2010-2025) were not collected with consent for external data sharing, and applicable personal-information protection regulations (e.g., China's Personal Information Protection Law) together with institutional policy prohibit transfer of patient-level data outside the sponsoring hospital. The study's primary deliverable is the UroAgent model and aggregated performance results, which will be disseminated through peer-reviewed publications and the trial registry record. Collaboration requests will be considered case-by-case under institutional approval.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No