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

April 16, 2025 updated by: Kang Zhang, The Eye Hospital of Wenzhou Medical University

AI-Agent Assisted Automation for Diagnosing and Predicting Patients Using Electronic Health Records and Multimodal Data

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.

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Detailed Description

This multi-center, retrospective clinical study is designed to evaluate the application and effectiveness of an AI agent in the medical decision-making process. The AI agent integrates and analyzes multimodal data, including electronic health records (EHR) and various imaging data (e.g., X-rays, MRIs, CT scans, ultrasounds) to predict and diagnose a range of diseases. By leveraging the power of machine learning and data fusion techniques, the AI agent can identify patterns in large and complex datasets, offering insights that may not be immediately apparent through traditional diagnostic methods.The study will compare the AI agent's diagnostic accuracy and disease prediction capabilities with traditional diagnostic practices to assess its potential benefits in clinical settings. Key questions include whether the AI agent can assist in early diagnosis, predict disease progression, and support healthcare professionals in making personalized treatment decisions. Participants will not be required to undergo any additional interventions; they will only provide historical health data, including EHR and relevant imaging data, which will be analyzed by the AI agent. The AI system will then use this data to assist healthcare providers by offering predictions and diagnostic suggestions based on the analysis of the multimodal information. The ultimate goal is to determine whether this AI-driven approach can improve diagnostic accuracy, optimize treatment strategies, and enhance patient outcomes in clinical practice.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Estimated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
2000000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact

The name and contact information for the person who can answer enrollment questions for a clinical study. Each location where the study is being conducted may also have a specific contact, who may be better able to answer those questions.

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China
        • Recruiting
        • Sun Yat-Sen Memorial Hospital
        • Contact:
      • Guangzhou, Guangdong, China
      • Guangzhou, Guangdong, China
        • Recruiting
        • Sun Yat-sen University Cancer Hospital
        • Contact:
    • Sichuan
      • Chengdu, Sichuan, China
        • Recruiting
        • West China Hospital
        • Contact:
    • Zhejiang
      • Wenzhou, Zhejiang, China
        • Recruiting
        • First Affiliated Hospital of Wenzhou Medical University
        • Contact:
      • Wenzhou, Zhejiang, China
        • Recruiting
        • Second Affiliated Hospital of Wenzhou Medical University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

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.

Description

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).

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort

Group / Cohort

A group or subgroup of participants in an observational study that is assessed for biomedical or health outcomes.
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.

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
Area Under the Curve (AUC)
Time Frame: 1 year
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
Time Frame: 1 year
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 Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Sensitivity (True Positive Rate)
Time Frame: 1 year
Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders.
1 year
Specificity (True Negative Rate)
Time Frame: 1 year
Specificity measures the ability of the AI model to correctly identify cases without diseases, ensuring that healthy mothers and infants are correctly identified as negative.
1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
The Eye Hospital of Wenzhou Medical University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
July 1, 2023

Primary Completion (Estimated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
July 1, 2025

Study Completion (Estimated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
July 1, 2025

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
January 19, 2025

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
January 19, 2025

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
January 24, 2025

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
April 17, 2025

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
April 16, 2025

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
April 1, 2025

More Information

Terms related to this study

Keywords

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • AI-agent

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Cookie Settings

We use cookies to ensure that we give you the best experience on our website. For more details carefully read our Privacy and Cookies Policy. ...>>>You can change your preferences or withdraw your consent at any time by deleting the cookies from your website or computer as described in the policy. Please accept it and choose your preferences by ticking the corresponding boxes in the "Manage Settings" section below. Please carefully read our Terms of Service before you proceed with using any part of this website.
«Manage Settings