AI-assisted cancer diagnosis using electronic health records
Official title: AI-Based Cancer Diagnosis and Prediction Using Electronic Health Records
Run by The Eye Hospital of Wenzhou Medical University · NCT06791473 · Data updated 2026-11-12
This study is testing if an AI tool can help doctors find cancer earlier and more accurately by using information from patients' health records.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 0 Years to 90 Years |
| Sex | All |
| Enrollment | 1000000 (estimated) |
| Sponsor | The Eye Hospital of Wenzhou Medical University Academic / other |
| Locations | 7 sites (Guangzhou, Guangdong and 6 other locations) |
| Status | Recruiting |
| Started | January 19, 2025 |
| Main results expected | October 1, 2025 |
| Study ends | October 1, 2025 |
| Registry record updated | July 30, 2025 |
| Trial ID | NCT06791473 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates include patients with comprehensive electronic health records and no severe cognitive impairments.
✕Probably not a fit
Patients with incomplete electronic health record data or severe cognitive disorders 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-driven predictive model for identifying and diagnosing cancer by utilizing multimodal health data from electronic health records. The study aims to enhance diagnostic accuracy and speed by integrating various patient data, including medical history, laboratory results, imaging data, and genetic markers. By leveraging advancements in artificial intelligence, the goal is to improve early detection of cancers, optimize clinical workflows, and provide personalized treatment options for patients. Ultimately, this approach seeks to enhance patient survival rates through timely and accurate diagnosis.
Why it matters
Potential benefit: If successful, this study could significantly improve early cancer detection and treatment outcomes for patients.
How similar studies have gone: Other studies utilizing AI for cancer diagnosis 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、Patients with comprehensive electronic health records (EHRs), including medical history, laboratory test results, imaging data, and genetic data (if available). 2\. Individuals without severe cognitive impairments or conditions that would prevent them from providing informed consent or participating in the study. 3\. Parents or guardians must provide informed consent for minors, while adult participants must provide informed consent for themselves. Exclusion Criteria: 1. Patients with incomplete or missing key electronic health record data or insufficient follow-up data. 2. Individuals with severe cognitive disorders or other terminal illnesses that would prevent meaningful participation. 3. Pregnant women (although pediatric cancers are being considered, pregnant women would be excluded for safety reasons).
Where it's running
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Guangzhou Women and Children's Medical Center
Guangzhou, Guangdong, China Recruiting -
Nanfang Hospital
Guangzhou, Guangdong, China Recruiting -
Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
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
Show 1 more location
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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.