ArticleCurrent cardiovascular risk reports2024
Artificial Intelligence to Promote Racial and Ethnic Cardiovascular Health Equity.
Article in Current cardiovascular risk reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Application of AI and digital health tools in public health management of T2DM: from mechanism prediction to personalized treatment.Frontiers in public health · 2026Review
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Purpose of Review: The integration of artificial intelligence (AI) in medicine holds promise for transformative advancements aimed at improving healthcare outcomes. Amidst this promise, AI has been envisioned as a tool to detect and mitigate racial and ethnic inequity known to plague current cardiovascular care. However, this enthusiasm is dampened by the recognition that AI itself can harbor and propagate biases, necessitating a careful approach to ensure equity. This review highlights topics in the landscape of AI in cardiology, its role in identifying and addressing healthcare inequities, promoting diversity in research, concerns surrounding its applications, and proposed strategies for fostering equitable utilization. Recent Findings: Artificial intelligence has proven to be a valuable tool for clinicians in diagnosing and mitigating racial and ethnic inequities in cardiology, as well as the promotion of diversity in research. This promise is counterbalanced by the cautionary reality that AI can inadvertently perpetuate existent biases stemming from limited diversity in training data, inherent biases within datasets, and inadequate bias detection and monitoring mechanisms. Recognizing these concerns, experts emphasize the need for rigorous efforts to address these limitations in the development and deployment of AI within medicine. Summary: Implementing AI in cardiovascular care to identify and address racial and ethnic inequities requires careful design and execution, beginning with meticulous data collection and a thorough review of training datasets. Furthermore, ensuring equitable performance involves rigorous testing and continuous surveillance of algorithms. Lastly, the promotion of diversity in the AI workforce and engagement of stakeholders are crucial to the advancement of equity to ultimately realize the potential for artificial intelligence for cardiovascular health equity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.