Large Language Models To Improve the Quality of Care of Cardiology Patients
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorStanford University
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Board certified or board eligible Cardiologist.
Disqualifiers
Not currently practicing clinically
Trial design
Design model
Parallel
Treatments tested in this trial
Large Language Model
Other interventionThe intervention is a Large Language Model.
Treatment groups
12 Participants
are divided into 2 treatment groupsGroup A: Large Language ModelActive comparator 1 intervention
Group B: Usual resourcesNo intervention 0 interventions
Trial outcomes
Primary outcomes
1
Subspecialist Preference
The primary outcome is the preference of the subspecialist between answers provided by a) Cardiologist with access to Large Language Model vs. b) Cardiologist without access to Large Language Model.
Time frame
Subspecialist evaluation will occur within 1 month of participant completing their assessmentSecondary outcomes
1
Participants perspective on use of Large Language model
Percentage of Cardiologists that felt the use of the Large Language Model helped their assessment.
Time frame
Within one-hourSponsors and contacts
Click on the lead sponsor to view all of their trials.
Stanford University
Lead sponsor
Google LLC.
Collaborator
Source ClinicalTrials.gov