ER-VISION-AI Study | NCT07727590

NCT07727590

Overview

NCT ID: NCT07727590
Lead Sponsor: Ewha Womans University Mokdong HospitalOTHER
Official Title: Multimodal Visual Language Model-Assisted Diagnostic Strategy in the Emergency Department: A Prospective Multicenter Randomized Controlled Trial (ER-VISION-AI Study)
Brief Summary: Prospective, multicenter, randomized, open-label, blinded-endpoint (PROBE-like) clinical trial evaluating whether physician-supervised Generative Pre-trained Transformer (GPT)-assisted multimodal diagnostic support improves diagnostic concordance in emergency department patients presenting with acute cardiopulmonary symptoms.
Conditions: Chest Pain
Dyspnea
Acute Cardiopulmonary Disease
Emergency Department Patients

Status and Timeline

Status: Not yet recruiting
Last Update Posted: Jul 27, 2026Actual
Start Date: Jan 1, 2027Estimated
Primary Completion Date: Dec 31, 2028Estimated
Completion Date

Participation Criteria

Ages Eligible for Study: Adult (18-64)Older Adult (65+)
Sexes Eligible for Study: All
Accepts Healthy Volunteers?: No
Eligibility Criteria: Inclusion Criteria:

Age ≥18 years

Presentation to a participating emergency department with acute cardiopulmonary symptoms, including chest pain, dyspnea, palpitations, syncope, dizziness, or fever accompanied by cardiopulmonary symptoms

Performance of both a standard 12-lead electrocardiogram and chest radiography during the initial emergency department evaluation

Availability of initial clinical assessment, vital signs, laboratory findings, and all mandatory clinical information required for the multimodal AI workflow

Expected emergency department observation or hospital admission for at least 24 hours

Ability and willingness to provide written informed consent

Exclusion Criteria:

Inability or refusal to provide written informed consent

Requirement for immediate life-saving intervention that precludes completion of the study workflow

Death before completion of the initial emergency department diagnostic assessment

Electrocardiographic quality insufficient for reliable physician or Artificial intelligence (AI) interpretation

Chest radiographic quality insufficient for reliable physician or Artificial intelligence (AI) interpretation

Cardiac pacing rhythm

Missing mandatory clinical information required for the multimodal Artificial intelligence (AI) workflow

Previous enrollment in the ER-VISION-AI trial

Inability to establish a blinded adjudicated reference diagnosis

Study Plan

Study Type: Interventional
Phase: Not Applicable
Enrollment: 1,000Estimated
Primary Purpose: Diagnostic
Allocation: Randomized
Interventional Model: Parallel Assignment
Masking: None (Open Label)

Contacts and Locations

Central Contacts: Yeji Kim, PhDContact
lexie6169@gmail.com+82-10-2724-7740

Similar Studies

Participant Group/Arms

GPT-assisted multimodal visual language model (VLM) diagnostic strategyExperimental

Participants receive physician-supervised Generative Pre-trained Transformer (GPT)-assisted multimodal diagnostic support integrating electrocardiography, chest radiography, structured clinical information, laboratory findings, vital signs, and relevant medical history. Treating physicians remain responsible for all diagnostic and therapeutic decisions.

Diagnostic Test: Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support

Conventional physician-guided diagnostic strategyActive Comparator

Participants undergo standard emergency department diagnostic evaluation according to routine clinical practice without Generative Pre-trained Transformer (GPT)-assisted diagnostic support.

Diagnostic Test: Conventional emergency department diagnostic evaluation