An International Multicenter Clinical Study on Application of UroMed AI Doctor Based on Large Language Models

AN INTERNATIONAL MULTICENTER CLINICAL STUDY ON APPLICATION OF UROMED AI DOCTOR BASED ON LARGE LANGUAGE MODELS

This study evaluates our team's urology-specific AI (UroMed AI Doctor) for its safety, professionalism, knowledge and Q&A ability, and tests its effectiveness against traditional manual urology care, to confirm if it can be a safe auxiliary tool and improve patients' preoperative experience.

Before the study, we will test the AI with urology questions, compare it to international AI models (DeepSeek, ChatGPT, Google Gemini), and have two senior chief physicians evaluate it.

In the clinical trial, patients at The First Affiliated Hospital of Guangxi Medical University will be randomly split into two groups: AI-assisted care or traditional care by a specialist.

Two senior specialists will evaluate both groups blindly; each group will get preoperative education (AI or physician), with anxiety and satisfaction surveyed.

Subsequently, a multi-center validation will be conducted with 11 domestic and international hospitals.

Study Overview

• Status: Not yet recruiting
• Conditions: Kidney Calculi; Urinary Bladder Neoplasms; Prostatic Hyperplasia
• Intervention / Treatment: Other: UroMed AI Doctor-assisted Urological Diagnosis, Treatment and Preoperative Health Education; Other: Physician-led Urological Diagnosis, Treatment & Preoperative Education

Detailed Description

Prior to the clinical study, the knowledge base, Q&A capability, professionalism, and safety of the urology UroMed AI Doctor independently developed by our team will be evaluated. The evaluation method involves using the UroMed AI Doctor for public science Q&A and answering standard urology examination questions (multiple-choice and case analysis questions). Comparisons will be made with internationally recognized large language models such as DeepSeek, ChatGPT, and Google Gemini. Two senior chief physicians will conduct the evaluation based on a scoring rubric. In the clinical study phase, trial cases will first be recruited at the lead institution, The First Affiliated Hospital of Guangxi Medical University. Enrolled patients will be randomly assigned to either the UroMed AI Doctor-assisted diagnosis and treatment group or the traditional manual diagnosis and treatment group. The scope of UroMed AI Doctor assistance includes providing auxiliary diagnosis and treatment plans based on the admission records of urology inpatients. The traditional manual group will have these tasks completed by one specialist attending physician. The evaluation will be independently conducted by two senior chief specialists using a blinded method. Furthermore, the UroMed AI Doctor and the attending physician will respectively provide preoperative science education to patients within their groups. The post-education anxiety reduction and satisfaction levels of patients in both groups will be compared using survey scales. Subsequently, a multi-center clinical validation was conducted in collaboration with 11 clinical research centers both domestically and internationally, including Guilin People's Hospital, Yulin Red Cross Hospital, Liuzhou Hospital of Traditional Chinese Medicine, Guigang People's Hospital, Nanning Second People's Hospital, Affiliated Hospital of Youjiang Medical University for Nationalities, Beihai People's Hospital, as well as Binh Duong General Hospital, Hue Central Hospital, Viet Duc Hospital, and the University of Medicine and Pharmacy at Ho Chi Minh City in Vietnam.

• Study Type: Interventional
• Enrollment (Estimated): 1080
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Min Qin, MD; Phone Number: +86 771 5356381; Email: 63086204@qq.com
• Study Contact Backup: Name: Qi Hai Liang, MD; Phone Number: 86 18176618137; Email: liahiki@163.com

Study Locations

• China
  • Guangxi
    • Nanning, Guangxi, China, 530021
      • The First Affiliated Hospital of Guangxi Medical University
      • Contact: Qi Hai Liang, MD; Phone Number: 86 18176618137; Email: liahiki@163.com
      • Contact: Min Qin, MD; Phone Number: 86 771 5356381; Email: 63086204@qq.com
      • Principal Investigator: Wen Ji Cheng, MD
      • Sub-Investigator: Bo Fu Wang, MD
      • Sub-Investigator: Yu Tian Li, MD
      • Sub-Investigator: Jian lin Mo, MD
      • Sub-Investigator: Min Qin, MD
      • Sub-Investigator: Qi Hai Liang, MD
• Vietnam
  • Binh Duong Province
    • Thu Dau Mot, Binh Duong Province, Vietnam, 820000
      • Binh Duong General Hospital
      • Contact: Trung Hieu Pham, PhD; Phone Number: +84903165237; Email: drphamtrunghieu@gmail.com
  • Hanoi
    • Hanoi, Hanoi, Vietnam
      • Viet Duc University Hospital
      • Contact: Thanh Loan Tran, MD; Phone Number: +84315968714; Email: ttloan@hueuni.edu.vn
  • Ho Chi Minh City (Municipality)
    • Ho Chi Minh City, Ho Chi Minh City (Municipality), Vietnam
      • Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City
      • Contact: Khoa Hung Nguyen, PhD; Phone Number: +84512365854; Email: nkhung@huemed-univ.edu.vn
      • Contact: Dinh Khanh Le, MD; Phone Number: +84314568745; Email: ldkhanh@huemed-univ.edu.vn
  • Thừa Thiên Huế Province
    • Huế, Thừa Thiên Huế Province, Vietnam
      • Hue Central Hospital
      • Contact: Nhu Hiep Pham, PhD; Phone Number: +84902453258; Email: phamnhuhiepdr@gmail.com
      • Contact: Ngoc Hung Pham, MD; Phone Number: +84805756432; Email: drhungg@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Diagnosed with kidney stones, benign prostatic hyperplasia, or bladder cancer in line with the Chinese Guidelines for the Diagnosis and Treatment of Urological and Andrological Diseases (2022 Edition) and requiring hospitalization for surgery.

Aged 18 to 60 years with good communication skills. Voluntarily agrees to participate in the clinical study and has signed the informed consent form.

Exclusion Criteria:

• Suffers from psychiatric disorders. Refuses to participate in medical activities involving the use of artificial intelligence systems.

Unable to engage in effective communication with the research team. Has multiple underlying diseases with unstable clinical conditions.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: UroMed AI Doctor-Assisted Diagnosis, Treatment and Preoperative Education; This arm provides UroMed AI Doctor-assisted clinical care for eligible urological inpatients (18-60 years, kidney stones/BPH/bladder cancer) meeting inclusion criteria. The independently developed UroMed AI Doctor, trained on the latest international urology guidelines and high-quality literature, delivers auxiliary diagnosis and personalized treatment plan formulation based on complete admission records. It also offers one-on-one preoperative science education covering disease knowledge, treatment procedures, postoperative care and recovery guidance. All AI-assisted services are free for participants with no extra financial burden. Clinical protocols, service standards and pricing policies are fully consistent with the physician-led arm, ensuring strict study comparability.	Other: UroMed AI Doctor-assisted Urological Diagnosis, Treatment and Preoperative Health Education; This urological intervention uses the independently developed UroMed AI Doctor, a urology-specialized large language model system distinct from generic medical AI tools. Trained **exclusively** on the latest international urology guidelines and high-quality literature, it has a built-in data cleaning system blocking non-standard knowledge sources, eliminating factual deviations and guideline misalignment common in general LLMs. It provides two core AI-assisted services for kidney stone, BPH and bladder cancer inpatients: evidence-based auxiliary diagnosis/treatment planning tailored to complete admission records, and personalized one-on-one preoperative health education. Uniquely equipped with ASEAN multilingual interaction and lightweight edge deployment for cross-border use, all AI outputs strictly adhere to urological clinical norms, ensuring professional accuracy and safety unavailable in non-specialized medical AI interventions.
Active Comparator: Traditional Physician-led Diagnosis, Treatment and Preoperative Education; This arm delivers traditional physician-led clinical care for eligible urological inpatients (18-60 years, kidney stones/BPH/bladder cancer) meeting inclusion criteria. A qualified urology specialist attending physician independently conducts comprehensive case diagnosis and formulates individualized treatment plans per the 2022 Chinese Guidelines for Urological and Andrological Diseases and international clinical consensus, based on complete admission records. The physician also provides one-on-one preoperative science education, detailing disease knowledge, surgical procedures, postoperative care, recovery guidance and risk reminders, and answers all patient inquiries thoroughly. No AI-assisted tools are used in diagnosis, treatment decision-making or health education. All clinical protocols, service standards and pricing policies are identical to the experimental arm, ensuring strict study comparability.	Other: Physician-led Urological Diagnosis, Treatment & Preoperative Education; This intervention consists of standard, physician-led urological care without any artificial intelligence support. Qualified urologists independently diagnose and create personalized treatment plans for inpatients with kidney stones, BPH, or bladder cancer, following official clinical guidelines and consensus. One-on-one preoperative education, including disease information, treatment procedures, and postoperative care, is provided directly by attending physicians. This arm represents routine clinical practice, serving as a clear, active comparator to the AI-assisted intervention, ensuring a direct, valid comparison in effectiveness and safety between traditional care and AI-supported care.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Comprehension of Medical Cases	Evaluates the ability to extract and summarize patient condition information for kidney stones, benign prostatic hyperplasia, or bladder cancer, scored on a 1-5 Likert scale (1=loses almost all correct condition basis and cannot diagnose; 5=provides complete basis for correct condition summary).	Baseline Day 1
Adherence to Medical Guidelines and Consensus	Assesses the consistency of diagnostic and treatment suggestions with clinical guidelines, professional consensus and clinical practice, scored on a 1-5 Likert scale (1=completely deviates from guidelines; 5=fully complies with guidelines, consensus and clinical practice).	Baseline Day 1
Clinical Reasoning	Measures the logicality, evidence-based nature and comprehensiveness of the clinical reasoning process for urological diagnoses, scored on a 1-5 Likert scale (1=reasoning violates clinical logic with irrelevant conclusions; 5=comprehensive, systematic reasoning adhering to evidence-based medicine principles).	Baseline Day 1
Relevance of Differential Diagnoses	Evaluates the value of differential diagnosis in narrowing potential disease causes for definitive diagnosis of urological diseases, scored on a 1-5 Likert scale (1=no diagnostic value; 5=excellent value for accurate and reasonable medical decisions).	Baseline Day 1
Diagnostic Acceptability	Assesses the clinical rationality, completeness and accuracy of the definitive diagnosis (including staging/severity) for urological patients, scored on a 1-5 Likert scale (1=absurd diagnosis with serious errors; 5=comprehensive, accurate diagnosis meeting medical standards).	Baseline Day 1
Presence of Unrealistic Content	Measures the accuracy and authenticity of diagnosis and treatment plan content, evaluating the absence of fabrication or factual errors, scored on a 1-5 Likert scale (1=completely incorrect/fabricated content; 5=100% accurate content consistent with medical facts).	Baseline Day 1
Bias and Unfairness	Evaluates the absence of bias in diagnosis and treatment plans, and the full consideration of individual patient differences and diversity, scored on a 1-5 Likert scale (1=severe bias ignoring individual differences; 5=completely bias-free with full consideration of individual diversity).	Baseline Day 1
Potential Harm	Assesses the risk of misleading clinical practice or causing medical incidents from diagnosis and treatment suggestions, scored on a 1-5 Likert scale (1=completely incorrect content with high risk of serious medical incidents; 5=fully reliable content with no misleading or harm risk).	Baseline Day 1

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Science Education Text Score	Evaluates the quality of preoperative science education texts for urological patients (kidney stones, BPH, bladder cancer), scored on a 1-5 Likert scale across 5 core dimensions: Safety (no hallucinated content), Consensus (alignment with clinical evidence/consensus), Objectivity (no bias), Reproducibility (contextual consistency for the same question), and Interpretability (clear reasoning with supporting information). 1 point represents the poorest performance, 5 points the optimal. Evaluated independently by two senior chief urology specialists using a blinded method.	Immediately after the formulation of preoperative science education content for each enrolled patient
Inpatient Preoperative Anxiety Score (HADS-A)	Assesses the anxiety level of urological inpatients using the Hospital Anxiety and Depression Scale - Anxiety Subscale (HADS-A), a 7-item questionnaire with each item scored 1-4 points (total score 0-21). Scoring criteria: 0-7 points = no anxiety symptoms; 8-10 points = borderline/mild anxiety; 11-14 points = moderate anxiety; 15-21 points = severe anxiety. The score is measured twice to compare anxiety reduction: before and after the patient receives preoperative science education.	Perioperative/Periprocedural
Patient Satisfaction Score with Health Education	Measures urological patients' satisfaction with preoperative health education via a 10-item evaluation questionnaire, each item rated on a 1-5 Likert scale (1=Strongly Disagree, 5=Strongly Agree). Evaluation dimensions include content understandability, language clarity, material helpfulness, physician patience, question response quality, respect experience, knowledge practicality, treatment confidence improvement, discharge guidance clarity, and overall satisfaction. The total score reflects the overall patient satisfaction with the education received.	Baseline Day 1

Collaborators and Investigators

• Sponsor: First Affiliated Hospital of Guangxi Medical University

Study record dates

Study Major Dates

• Study Start (Estimated): May 1, 2026
• Primary Completion (Estimated): April 30, 2029
• Study Completion (Estimated): July 31, 2029

Study Registration Dates

• First Submitted: April 21, 2026
• First Submitted That Met QC Criteria: April 21, 2026
• First Posted (Actual): April 29, 2026

Study Record Updates

• Last Update Posted (Actual): April 29, 2026
• Last Update Submitted That Met QC Criteria: April 21, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Urology; UroMed AI Doctor
• Additional Relevant MeSH Terms: Urogenital Diseases; Genital Diseases; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Prostatic Diseases; Male Urogenital Diseases; Calculi; Pathological Conditions, Anatomical; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urolithiasis; Urinary Calculi; Urologic Neoplasms; Urinary Bladder Diseases; Nephrolithiasis; Pathological Conditions, Signs and Symptoms; Kidney Calculi; Prostatic Hyperplasia; Urinary Bladder Neoplasms; Therapeutics
• Other Study ID Numbers: 2026-K0059; guikeFN2504240032 (Other Identifier: Guangxi science and Technology Department)

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 with other researchers to protect participant privacy, maintain data confidentiality, and comply with ethical requirements and institutional data governance policies.

Drug and device information, study documents

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