AI-Assisted Medical Decision-Making

NCT06846229 | Recruiting

• 1 other identifier: AI Prediction
• Study Type: observational
• Target: 50,000,000
• Locations: 1 country
• Sites: 2

Brief Summary

The study builds and applies an AI model to help doctors predict patient diagnoses and outcomes, such as survival or hospital stay. Real-time, multimodal data (labs, vital signs, history, imaging) from hospital records will be used. Patients will be tracked to compare the AI's performance with standard care. The goal is to improve diagnosis and treatment accuracy in a real-world, prospective study.

Conditions

Real-world Study

Keywords

diagnosis; prediction; AI; outcome

Outcome Measures

Primary Outcomes (4)

• 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

• Overall Hospital Resource Utilization Improvement

  The percentage reduction in overall hospital resource use (e.g., bed days, ICU admissions, diagnostic tests) attributed to AI-assisted decision-making, expressed as a percentage.

  1 year

• Population-Level Diagnostic Accuracy Enhancement

  The overall improvement in diagnostic accuracy across all hospital patients (e.g., percentage of correct diagnoses or reduction in misdiagnoses) facilitated by the AI model, expressed as a percentage or ratio.

  1 year

• System-Wide Reduction in Adverse Event Rates

  The percentage reduction in major adverse events (e.g., mortality, severe complications, or prolonged stays) across all hospital patients due to AI-assisted decision-making, expressed as a percentage.

  1 year

Secondary Outcomes (4)

• Overall Improvement in Hospital Patient Outcomes

  1 year

• Enhancement of Healthcare System Efficiency

  1 year

• Population Health Impact Score

  1 year

• Long-Term Public Health Benefit Index

  1 year

Study Arms (1)

Hospital-Wide Patient Cohort

Other: AI-associated strategy

Interventions

AI-associated strategy OTHER

The intervention in this study involves an AI system that leverages multimodal data fusion to support the clinical decision-making and evaluation of diseases. Patients in this cohort will undergo standard examinations, with clinical decisions guided by the recommendations generated by the AI system.

Hospital-Wide Patient Cohort

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

This study includes all patients admitted to any hospital department, with real-time electronic health record data (e.g., labs, vital signs, history, imaging). Participants must consent to data collection.

You may qualify if:

• Patients admitted to any department of the hospital (e.g., ICU, general wards, emergency, outpatient services) during the study period.
• Patients with available real-time electronic health record (EHR) data, including at least two of the following: laboratory results, vital signs, medical history, and imaging data.

You may not qualify if:

• Patients currently enrolled in another clinical trial that could interfere with data collection or outcomes of this study.

Sponsors & Collaborators

• The Eye Hospital of Wenzhou Medical University: lead

Study Sites (2)

First Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

RECRUITING

Second Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

RECRUITING

MeSH Terms

Conditions

Disease

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Fei Liu, MD

CONTACT

+86 13810512704; liufei_2359@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Scientist

Study Record Dates

• First Submitted: February 24, 2025
• First Posted: February 26, 2025
• Study Start: February 24, 2025
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: March 3, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (2)