Large Language Models for Cardiomyopathy

Overseen By Jack W O'Sullivan, MBBS, DPhil

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Stanford University

Disqualifiers: Not practicing clinically

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

12 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether participants must stop taking their current medications.

What data supports the effectiveness of the treatment Large Language Model for cardiomyopathy?

Research suggests that using artificial intelligence, like large language models, can help analyze medical images and patient data to better understand and predict heart conditions, including cardiomyopathies. This approach could lead to more personalized and effective treatments by identifying specific disease patterns and potential new drug targets. ^{1 2 3 4 5}

Is there any safety data available for Large Language Models used in medical treatments?

The research does not provide specific safety data for Large Language Models used in medical treatments, but it highlights the importance of understanding drug safety through animal and human studies, and the challenges in predicting adverse events. ^{6 7 8 9 10}

How does the Large Language Model treatment differ from other treatments for cardiomyopathy?

The Large Language Model treatment is unique because it uses artificial intelligence to analyze complex data from cardiac imaging and other sources, providing a more personalized and precise approach to diagnosing and managing cardiomyopathy compared to traditional methods. ^{11 12 13 14 15}

What is the purpose of 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.

Research Team

Euan A Ashley, BSc, MB ChB, DPhil

Principal Investigator

Stanford University

Eligibility Criteria

This trial is for board-certified or board-eligible cardiologists who are currently practicing clinically. It's not open to those who aren't actively seeing patients.

Inclusion Criteria

Board certified or board eligible Cardiologist

Exclusion Criteria

Not currently practicing clinically

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Assessment

General cardiologists are randomized to manage real patient cases with or without AI assistance

1 month

Multiple assessments per participant

Evaluation

Subspecialty experts evaluate the cardiologists' responses using a standardized rubric

1 month

Follow-up

Participants are monitored for feedback on the use of the Large Language Model

1 hour

Treatment Details

Interventions

• Large Language Model

Trial Overview The study is testing whether large language models (AI tools) can help general cardiologists make better decisions in managing real patient cases of genetic heart diseases, compared to using traditional decision support tools.

Participant Groups

Group I: Large Language Model Active Control 1 Intervention

This group will be given access to a Large Language Model

Group II: Usual resources Active Control 1 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 Is Running the Clinical Trial?

Stanford University

Lead Sponsor

Trials

2,527

Recruited

17,430,000+

Google LLC.

Industry Sponsor

Trials

12

Recruited

31,700+

Findings from Research

A comprehensive analysis of 3,290 approved drugs and over 1.6 million reported adverse events revealed that animal studies can effectively predict human safety outcomes, particularly for conditions like QT prolongation and arrhythmias observed in dogs.

Despite the overall predictivity of animal safety data for humans, the study highlighted challenges in data curation and changes in methodologies over time that can affect the reliability of these analyses.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A big data approach to the concordance of the toxicity of pharmaceuticals in animals and humans. Clark, M., Steger-Hartmann, T.[2018]

Dilated Cardiomyopathy (DCM) patients often face significant risks despite treatment, indicating that traditional measures like left ventricular ejection fraction are insufficient for predicting outcomes, highlighting the need for better disease characterization.

Cardiac MRI (CMR) combined with advanced artificial intelligence techniques, particularly deep learning, can enhance the assessment of DCM by providing detailed functional and tissue characterization, which may improve diagnosis and risk stratification for patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Role of AI in Characterizing the DCM Phenotype. Asher, C., Puyol-Antón, E., Rizvi, M., et al.[2023]

References

1. Netherlands pubmed.ncbi.nlm.nih.gov

Applicability and performance of heart failure prognostic scores in dilated cardiomyopathy: the real-world experience of an Italian referral center for cardiomyopathies. [2023]

2. United States pubmed.ncbi.nlm.nih.gov

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies. [2023]

3. England pubmed.ncbi.nlm.nih.gov

Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data. [2021]

4. England pubmed.ncbi.nlm.nih.gov

Developing Clinical Risk Prediction Models for Worsening Heart Failure Events and Death by Left Ventricular Ejection Fraction. [2023]

5. Switzerland pubmed.ncbi.nlm.nih.gov

Machine learning models in heart failure with mildly reduced ejection fraction patients. [2022]

6. Netherlands pubmed.ncbi.nlm.nih.gov

A big data approach to the concordance of the toxicity of pharmaceuticals in animals and humans. [2018]

7. Netherlands pubmed.ncbi.nlm.nih.gov

A systematic review of validated methods to capture myopericarditis using administrative or claims data. [2018]

8. United States pubmed.ncbi.nlm.nih.gov

Information-Derived Mechanistic Hypotheses for Structural Cardiotoxicity. [2019]

9. Germany pubmed.ncbi.nlm.nih.gov

Can Natural Language Processing Improve the Efficiency of Vaccine Adverse Event Report Review? [2017]

10. England pubmed.ncbi.nlm.nih.gov

Ontology-based systematical representation and drug class effect analysis of package insert-reported adverse events associated with cardiovascular drugs used in China. [2019]

11. United States pubmed.ncbi.nlm.nih.gov

Machine learning-based classification and diagnosis of clinical cardiomyopathies. [2021]

12. Switzerland pubmed.ncbi.nlm.nih.gov

Classification of Cardiomyopathies from MR Cine Images Using Convolutional Neural Network with Transfer Learning. [2021]

13. England pubmed.ncbi.nlm.nih.gov

Artificial intelligence study on left ventricular function among normal individuals, hypertrophic cardiomyopathy and dilated cardiomyopathy patients using 1.5T cardiac cine MR images obtained by SSFP sequence. [2022]

14. Switzerland pubmed.ncbi.nlm.nih.gov

The Role of AI in Characterizing the DCM Phenotype. [2023]

15. United States pubmed.ncbi.nlm.nih.gov

Machine Learning Outcome Prediction in Dilated Cardiomyopathy Using Regional Left Ventricular Multiparametric Strain. [2021]