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

The Eye Hospital of Wenzhou Medical University2,000,000 enrolled

Overview

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.

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

OBSERVATIONAL

Enrollment

2,000,000

Conditions

AI Agent

Eligibility

Sex: ALLHealthy volunteers:

Medical Language ↔ Plain English

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).

Locations (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

Outcomes

Primary Outcomes

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).

Time frame: 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.

Time frame: 1 year

Secondary Outcomes

Sensitivity (True Positive Rate)

Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders.

Time frame: 1 year

Specificity (True Negative Rate)

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.

Time frame: 1 year

Central Contacts

Fei Liu, MD

CONTACT

+86 13810512704 liufei_2359@163.com

Data from ClinicalTrials.gov

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