Using AI to Diagnose and Predict Diseases from Health Records and Imaging Data
Official title: AI-Agent Assisted Automation for Diagnosing and Predicting Patients Using Electronic Health Records and Multimodal Data
Run by The Eye Hospital of Wenzhou Medical University · NCT06791499 · Data updated 2026-11-12
This study is testing whether an AI tool can help doctors diagnose and predict diseases better by looking at patients' health records and medical images.
Quick facts
| Study type | Observational |
|---|---|
| Sex | All |
| Enrollment | 2000000 (estimated) |
| Sponsor | The Eye Hospital of Wenzhou Medical University Academic / other |
| Locations | 6 sites (Guangzhou, Guangdong and 5 other locations) |
| Status | Recruiting |
| Started | July 1, 2023 |
| Main results expected | July 2025 |
| Study ends | July 2025 |
| Registry record updated | April 17, 2025 |
| Trial ID | NCT06791499 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates include individuals with comprehensive electronic health records and relevant multimodal imaging data who have a confirmed diagnosis of one or more diseases.
✕Probably not a fit
Patients with ambiguous or unverifiable diagnoses or those with duplicate health records may not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational study evaluates the effectiveness of an AI agent designed to assist in diagnosing and predicting diseases by analyzing electronic health records (EHR) and multimodal imaging data. The AI agent employs advanced machine learning algorithms to process diverse health data sources, aiming to enhance the accuracy of medical diagnoses and predictions. By comparing the AI's performance with traditional diagnostic methods, the study seeks to determine its potential benefits in clinical decision-making. Participants will provide historical health data, which will be analyzed by the AI to generate diagnostic suggestions and predictions without requiring additional interventions.
Why it matters
Potential benefit: If successful, this AI-driven approach could significantly improve diagnostic accuracy and optimize treatment strategies for patients.
How similar studies have gone: Other studies utilizing AI for diagnostic purposes have shown promising results, indicating potential for success in this approach.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Participants must have comprehensive electronic health records (EHR) available, including demographic information, medical history, and laboratory results. 2. Participants must have available multimodal imaging data (e.g., X-rays, CT scans, MRIs, ultrasounds) relevant to their health condition. 3. Participants must have a confirmed diagnosis of one or more diseases or health conditions based on clinical records or imaging data. 4. Patients must provide consent for the use of their historical health data for research purposes. Exclusion Criteria: 1. Participants with ambiguous or unverifiable diagnoses that cannot be accurately categorized. 2. Duplicate or redundant patient data (e.g., repeated records of the same patient without clear differentiation).
Where it's running
-
Nanfang Hospital
Guangzhou, Guangdong, China Recruiting -
Sun Yat-Sen Memorial Hospital
Guangzhou, Guangdong, China Recruiting -
Sun Yat-sen University Cancer Hospital
Guangzhou, Guangdong, China Recruiting -
West China Hospital
Chengdu, Sichuan, China Recruiting -
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China Recruiting -
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China Recruiting
Study contacts
- Study coordinator: Fei Liu, MD
- Email: liufei_2359@163.com
- Phone: +86 13810512704
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.