Hypertrophic Cardiomyopathy (HCM)NCT06935253
Large Language Models To Improve the Quality of Care of Cardiology Patients
A study testing Large Language Model in people with Hypertrophic Cardiomyopathy (HCM) and Cardiomyopathy, run by Stanford University at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hypertrophic Cardiomyopathy (HCM), Cardiomyopathy, Genetic Disease
- What is being tested?
- Large Language Model
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 12 participants
- Where
- 1 siteUnited States
- Study dates
- 10 Jan 2025to Dec 2025
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Board certified or board eligible Cardiologist.
Who cannot join
- Not currently practicing clinically
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Stanford
Palo Alto, California, 94303
Jack W O'Sullivan, MD, PhD, 6503009129, jackos@stanford.edu
Euan A Ashley, MD, PhD, 650-736-7878, euan@stanford.edu
About this study
Official title: Towards Bridging Generalists to Subspecialists With Large Language Models
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.
Read the detailed description
Large language models have been shown to improve physician performance in simulated settings. Large language models have demonstrated promise in various healthcare contexts, including medical note-writing, addressing patient inquiries, and facilitating medical consultation. However, it remains uncertain whether large language models improve clinical reasoning of clinicians using real world cases.
Clinicians dedicate years of training to develop expertise, with clinical knowledge a key component. Clinicians have different areas of expertise, from generalists spanning diseases of all organ systems and patients of all ages, to subspecialists dedicated to often a handful of diseases effecting a specific organ. Both skill sets are vital to a well-functioning medical system, as generalists generally care for patients and refer to specialists when dedicated, specialty knowledge is required. There is a paucity of specialists, and thus the quality of triaging and referral to specialists is of upmost importance. We hypothesis that large language models may be able help generalists management complex patients, and improve their triage to specialists and subspecialists.
The scarcity of subspecialist medical expertise, particularly in rare, complex and life-threatening diseases, poses a significant challenge for healthcare delivery. This issue is particularly acute in cardiology where timely, accurate management determines outcomes. In this study, we will recruit General Cardiologists as participants who will be randomized to answer clinical management cases with or without access to a large language model. Each case is a real patient case of a patient referred to a subspeciality cardiovascular genetic cardiomyopathy clinic. Each case will be performed by two general cardiologists (one with access to a large language model and one without access). Each case has multiple components, and the participants will be asked to answer questions related to the management. Answers will be graded by independent, blinded subspeciality Cardiologists with expertise and training in genetic cardiomyopathies. An evaluation rubric was developed by 10 expert discussants.
- Being tested
- Large Language Model
- Keywords
- Large language models, Cardiology
- Medical subject headings
- Cardiomyopathy, Hypertrophic, Cardiomyopathies, Genetic Diseases, Inborn
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Large Language ModelOther
Also called AMIE (Articulate Medical Intelligence Explorer)
The intervention is a Large Language Model.
| Study group | Role | What they receive |
|---|---|---|
| Large Language Model | Active comparator | This group will be given access to a Large Language Model Receives: Other: Large Language Model |
| Usual resources | No intervention | Group will not be given access to a Large Language Model but will be encouraged to use any resources they usually use in their practice besides large language models (UpToDate, Dynamed etc). |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Subspecialist evaluation will occur within 1 month of participant completing their assessment
Secondary 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.
Measured: Within one-hour
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Apr 2025
- Study start10 Jan 2025
- Primary completionNov 2025 (estimated)
- Study completionDec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Hypertrophic Cardiomyopathy (HCM)Studies of the same condition, open and under way.
- More trials run by Stanford UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 12 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Stanford University
- Sponsor type
- University, hospital or other
- Collaborators
- Google LLC.
- Responsible party
- Principal investigator, Jack O'Sullivan, Stanford University
- Investigators
- Euan A Ashley, BSc, MB ChB, DPhil, Principal investigator, Stanford University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06935253
- Sponsor's protocol ID
- 78695
- Status last verified
- May 2025
- First submitted
- 11 Apr 2025
- Last updated
- 15 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The patient cases that will be used in this study will be deidentified and made publicly available. The code to conduct the statistical analysis will also be made available.
Study at a glance
From the registry record, last updated by the sponsor on 15 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Towards Bridging Generalists to Subspecialists With Large Language Models
- ClinicalTrials.gov ID
- NCT06935253
- Acronym / sponsor ID
- 78695
- Keywords
- Large language models, Cardiology
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 12 participants (estimated)
- Lead sponsor
- Stanford UniversityUniversity, hospital or other
- Collaborators
- Google LLC.
- Study start
- 10 Jan 2025 (actual)
- Primary completion
- Nov 2025 (estimated)
- Study completion
- Dec 2025 (estimated)
- First posted
- 20 Apr 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Large language models have been shown to improve physician performance in simulated settings. Large language models have demonstrated promise in various healthcare contexts, including medical note-writing, addressing patient inquiries, and facilitating medical consultation. However, it remains uncertain whether large language models improve clinical reasoning of clinicians using real world cases.
Clinicians dedicate years of training to develop expertise, with clinical knowledge a key component. Clinicians have different areas of expertise, from generalists spanning diseases of all organ systems and patients of all ages, to subspecialists dedicated to often a handful of diseases effecting a specific organ. Both skill sets are vital to a well-functioning medical system, as generalists generally care for patients and refer to specialists when dedicated, specialty knowledge is required. There is a paucity of specialists, and thus the quality of triaging and referral to specialists is of upmost importance. We hypothesis that large language models may be able help generalists management complex patients, and improve their triage to specialists and subspecialists.
The scarcity of subspecialist medical expertise, particularly in rare, complex and life-threatening diseases, poses a significant challenge for healthcare delivery. This issue is particularly acute in cardiology where timely, accurate management determines outcomes. In this study, we will recruit General Cardiologists as participants who will be randomized to answer clinical management cases with or without access to a large language model. Each case is a real patient case of a patient referred to a subspeciality cardiovascular genetic cardiomyopathy clinic. Each case will be performed by two general cardiologists (one with access to a large language model and one without access). Each case has multiple components, and the participants will be asked to answer questions related to the management. Answers will be graded by independent, blinded subspeciality Cardiologists with expertise and training in genetic cardiomyopathies. An evaluation rubric was developed by 10 expert discussants.
Study design
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Large Language Model | Active comparator | Other: Large Language ModelThis group will be given access to a Large Language Model |
| Usual resources | No intervention | –Group will not be given access to a Large Language Model but will be encouraged to use any resources they usually use in their practice besides large language models (UpToDate, Dynamed etc). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Board certified or board eligible Cardiologist.
Key exclusion criteria (1 listed)
- Not currently practicing clinically
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Large Language ModelAlso: AMIE (Articulate Medical Intelligence Explorer)The intervention is a Large Language Model. | Other | Large Language Model |
Primary outcome
Subspecialist Preference
Time frame: Subspecialist evaluation will occur within 1 month of participant completing their assessment
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.
1 secondary outcome
Participants perspective on use of Large Language model
Time frame: Within one-hour
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 May 2025.
- Individual participant dataSharing planned: Yes. The patient cases that will be used in this study will be deidentified and made publicly available. The code to conduct the statistical analysis will also be made available.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Euan A Ashley, BSc, MB ChB, DPhil principal investigator
Stanford University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Large Language Model in people with Hypertrophic Cardiomyopathy (HCM) and Cardiomyopathy, run by Stanford University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 15 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jack W O'Sullivan, MBBS, DPhil (+16507367878, jackos@stanford.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Subspecialist Preference (time frame: Subspecialist evaluation will occur within 1 month of participant completing their assessment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 May 2025.