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Large Language Models To Improve the Quality of Care of Cardiology Patients

NCT06935253 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 12

Last updated 2025-05-15

No results posted yet for this study

Summary

This study evaluates the impact of large language models (LLMs) versus traditional decision support tools on clinical decision-making in cardiology. General cardiologists will be randomized to manage real patient cases from a cardiovascular genetic cardiomyopathy clinic, with or without AI assistance. Each case will be assessed by two cardiologists, and their responses will be graded by blinded subspecialty experts using a standardized evaluation rubric.

Conditions

Interventions

OTHER

Large Language Model

The intervention is a Large Language Model.

Sponsors & Collaborators

Principal Investigators

  • Euan A Ashley, BSc, MB ChB, DPhil · Stanford University

Study Design

Allocation
RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-01-10
Primary Completion
2025-11-30
Completion
2025-12-31

Countries

  • United States

Study Locations

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Entities

Diseases
Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06935253 on ClinicalTrials.gov