Addressing hidden risks: Systematic review of artificial intelligence biases across racial and ethnic groups in cardiovascular diseases

Cau, Riccardo

First

;Pisu, Francesco;Suri, Jasjit S.;Saba, Luca

2025-01-01

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Abstract

Background: Artificial intelligence (AI)-based models are increasingly being integrated into cardiovascular medicine. Despite promising potential, racial and ethnic biases remain a key concern regarding the development and implementation of AI models in clinical settings. Objective: This systematic review offers an overview of the accuracy and clinical applicability of AI models for cardiovascular diagnosis and prognosis across diverse racial and ethnic groups. Method: A comprehensive literature search was conducted across four medical and scientific databases: PubMed, MEDLINE via Ovid, Scopus, and the Cochrane Library, to evaluate racial and ethnic disparities in cardiovascular medicine. Results: A total of 1704 references were screened, of which 11 articles were included in the final analysis. Applications of AI-based algorithms across different race/ethnic groups were varied and involved diagnosis, prognosis, and imaging segmentation. Among the 11 studies, 9 (82%) concluded that racial/ethnic bias existed, while 2 (18%) found no differences in the outcomes of AI models across various ethnicities. Conclusion: Our results suggest significant differences in how AI models perform in cardiovascular medicine across diverse racial and ethnic groups. Clinical relevance statement: The increasing integration of AI into cardiovascular medicine highlights the importance of evaluating its performance across diverse populations. This systematic review underscores the critical need to address racial and ethnic disparities in AI-based models to ensure equitable healthcare delivery.