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Artificial Intelligence in Healthcare and Education / Machine Learning in Healthcare · Journal article
Frontiers in Artificial Intelligence · July 15, 2026
A consensus or society position rather than new primary data.
This is a narrative review and position statement identifying systemic deficiencies in AI and precision medicine development for cardiovascular care, specifically the risk that AI models trained on non-diverse populations will embed historical disparities into clinical decision-support tools. The paper does not report new empirical evidence but argues that healthcare systems and institutions must actively restructure data collection, trial design, and governance before deploying AI in diverse cardiovascular populations.
Journal article. Younger and older adults with cardiovascular disease, with emphasis on racial and ethnic minority populations and female patients; also racially and ethnically diverse children with CVD..
Black patients experience higher prevalence of heart failure and hypertension, especially transthyretin amyloid cardiomyopathy HF, with Black women disproportionately affected. Racially and ethnically diverse children with cardiovascular disease have higher odds of mortality than White counterparts. AI models created with uncomprehensive data primarily drawn from White populations risk embedding historical differences into clinical decision-support systems.
Racially and ethnically diverse children with cardiovascular disease have higher odds of mortality than White counterparts.
Clinicians and institutions implementing or evaluating AI-based cardiovascular decision-support tools should recognize the risk of bias from training data skewed toward White populations and should demand diverse genomic datasets and equitable representation in validation cohorts before deployment. The paper recommends that institutions historically serving diverse populations lead future research to enhance fairness and accuracy.
An integrative review and expert position paper identifying gaps in AI/precision medicine implementation for diverse cardiovascular populations, calling for systemic restructuring before deployment rather than reporting new empirical evidence.
Quoted from the source exactly as published.
Clinicians and institutions implementing or evaluating AI-based cardiovascular decision-support tools should recognize the risk of bias from training data skewed toward White populations and should demand diverse genomic datasets and equitable representation in validation cohorts before deployment. The paper recommends that institutions historically serving diverse populations lead future research to enhance fairness and accuracy.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Artificial Intelligence (AI) has the potential to revolutionize medicine, particularly in the field of cardiology. There are significant diagnostics and treatments variabilities in the field of cardiovascular medicine that affects racial and ethnic racially and ethnically diverse populations as well as female patients across all age groups. The efforts put forth towards the development of AI and precision medicine within the cardiovascular practice do not fully account for existing variations in cardiovascular care delivery. AI models and precision medicine tools that were created with uncomprehensive data primarily drawn from White populations risk embedding historical differences into clinical decision-support systems. This paper outlines the integrative approach taken to review the current variabilities that persist within younger adults (<65 years) and older adults (≥ 65 years) who have cardiovascular disease. Additionally, genetic factors, limited access to care, health literacy, lack of insurance coverage and adherence are examined, as these are frequently cited as major contributors to health care adverse outcomes but remain under-researched and unresolved even with the expansion of Medicaid. For instance, Black patients experience higher prevalence of heart failure (HF) and hypertension, especially transthyretin amyloid cardiomyopathy HF, with Black women being disproportionately affected due to higher structural, environmental and clinical factors. Also, racially and ethnically diverse children with CVDs have higher odds of mortality than their White counterparts. The integration of AI in cardiovascular medicine must first be preceded by an active effort to restructure systems and reduce variable outcomes. Future research must prioritize diverse genomic datasets and equitable comprehensive representation in clinical trials. These initiatives are better served if they are driven by institutions that historically serve racially and ethnically diverse populations and communities to better enhance inclusion and fairness in electronic medical record keeping. Accordingly, cardiovascular medical practices and technology can progress forward with AI and precision medicine models that are both equitable and accurate.
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