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Implications of Bias in Artificial Intelligence: Considerations for Cardiovascular Imaging

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Abstract

Purpose of Review

Bias in artificial intelligence (AI) models can result in unintended consequences. In cardiovascular imaging, biased AI models used in clinical practice can negatively affect patient outcomes. Biased AI models result from decisions made when training and evaluating a model. This paper is a comprehensive guide for AI development teams to understand assumptions in datasets and chosen metrics for outcome/ground truth, and how this translates to real-world performance for cardiovascular disease (CVD).

Recent Findings

CVDs are the number one cause of mortality worldwide; however, the prevalence, burden, and outcomes of CVD vary across gender and race. Several biomarkers are also shown to vary among different populations and ethnic/racial groups. Inequalities in clinical trial inclusion, clinical presentation, diagnosis, and treatment are preserved in health data that is ultimately used to train AI algorithms, leading to potential biases in model performance. Despite the notion that AI models themselves are biased, AI can also help to mitigate bias (e.g., bias auditing tools).

Summary

In this review paper, we describe in detail implicit and explicit biases in the care of cardiovascular disease that may be present in existing datasets but are not obvious to model developers. We review disparities in CVD outcomes across different genders and race groups, differences in treatment of historically marginalized groups, and disparities in clinical trials for various cardiovascular diseases and outcomes. Thereafter, we summarize some CVD AI literature that shows bias in CVD AI as well as approaches that AI is being used to mitigate CVD bias.

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Funding

Marly van Assen receives research funding from Siemens Healthineers. Judy Gichoya is a 2022 Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program Award and declares support from RSNA Health Disparities grant (#EIHD2204), Lacuna Fund, and NIH (NIBIB) MIDRC grant under contracts 75N92020C00008 and 75N92020C00021.

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Authors and Affiliations

  1. Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA

    Marly van Assen, Gabrielle Gershon, Janice Newsome, Hari Trivedi & Judy Gichoya

  2. Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA

    Ashley Beecy

  3. Information Technology, NewYork-Presbyterian, New York, NY, USA

    Ashley Beecy

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  1. Marly van Assen
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MvA, GG, and JG wrote the main manuscript text; AB, JN, and HT provided information and references on additional topics. All authors reviewed the manuscript.

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Correspondence to Marly van Assen.

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Marly van Assen receives research funding from Siemens Healthineers. Judy Gichoya is a 2022 Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program Award and declares support from RSNA Health Disparities grant (#EIHD2204), Lacuna Fund, and NIH (NIBIB) MIDRC grant under contracts 75N92020C00008 and 75N92020C00021.

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van Assen, M., Beecy, A., Gershon, G. et al. Implications of Bias in Artificial Intelligence: Considerations for Cardiovascular Imaging. Curr Atheroscler Rep 26, 91–102 (2024). https://doi.org/10.1007/s11883-024-01190-x

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