AI-Agent for Automated Diagnosis and Predicting Using EHR and Multimodal Data

NCT06791499 | Recruiting

• 1 other identifier: AI-agent
• Study Type: observational
• Target: 2,000,000
• Locations: 1 country
• Sites: 6

Brief Summary

The goal of this clinical study is to evaluate the effectiveness of an AI agent in diagnosing and predicting diseases using electronic health records (EHR) and multimodal imaging data. The AI agent leverages advanced machine learning algorithms to process and analyze diverse health data sources, aiming to assist healthcare providers in making more accurate diagnoses and predictions.

Conditions

AI Agent

Keywords

AI agent

Outcome Measures

Primary Outcomes (2)

• Area Under the Curve (AUC)

  AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).

  1 year

• F1 Score

  The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.

  1 year

Secondary Outcomes (2)

• Sensitivity (True Positive Rate)

  1 year

• Specificity (True Negative Rate)

  1 year

Study Arms (1)

AI-Assisted Disease Prediction Using EHR and Imaging Data

This cohort consists of patients whose historical health data, including electronic health records (EHR) and multimodal imaging data (e.g., X-rays, MRIs, CT scans, ultrasounds), will be analyzed by an AI agent. The AI system will assist in diagnosing and predicting diseases by processing and integrating these diverse data sources. The primary focus is to evaluate the ability of the AI agent to identify patterns and predict disease progression with high accuracy. Participants will not be required to take any additional actions beyond providing their medical history and imaging data. The aim is to assess how well the AI system can support clinical decision-making and improve diagnostic outcomes based on the provided data.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The participants for this study will be selected from multiple healthcare centers and hospitals that maintain comprehensive electronic health records (EHR) and multimodal imaging data. The study population will include patients who have a variety of diseases or health conditions, with data available for diagnosis and disease progression. Participants will have confirmed diagnoses based on clinical records or imaging data, including but not limited to conditions captured by X-rays, CT scans, MRIs, and ultrasounds. Both those with complex health conditions and those with more common illnesses will be included to evaluate the AI system's diagnostic and predictive capabilities across a broad spectrum of cases.Participants from these centers will provide historical health data, and there will be no active intervention beyond the use of their existing clinical and imaging data for training and testing the AI system.

You may qualify if:

• Participants must have comprehensive electronic health records (EHR) available, including demographic information, medical history, and laboratory results.
• Participants must have available multimodal imaging data (e.g., X-rays, CT scans, MRIs, ultrasounds) relevant to their health condition.
• Participants must have a confirmed diagnosis of one or more diseases or health conditions based on clinical records or imaging data.
• Patients must provide consent for the use of their historical health data for research purposes.

You may not qualify if:

• Participants with ambiguous or unverifiable diagnoses that cannot be accurately categorized.
• Duplicate or redundant patient data (e.g., repeated records of the same patient without clear differentiation).

Sponsors & Collaborators

• The Eye Hospital of Wenzhou Medical University: lead

Study Sites (6)

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

Central Study Contacts

Fei Liu, MD

CONTACT

+86 13810512704; liufei_2359@163.com

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Scientist

Study Record Dates

• First Submitted: January 19, 2025
• First Posted: January 24, 2025
• Study Start: July 1, 2023
• Primary Completion: July 1, 2025
• Study Completion: July 1, 2025
• Last Updated: April 17, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (6)