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The clinicians’ perspectives on machine learning

Nature Cardiovascular Research volume 1, pages 189–190 (2022)Cite this article

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The intended purpose of machine learning (ML) in cardiovascular medicine is to help guide clinical diagnoses as well as promote scientific discovery. Whether ML is implemented by most clinical cardiologists and cardiovascular researchers will likely depend on the successful resolution of concerns fueling hesitancy to embrace ML. This commentary discusses caveats related to ML in clinical practice, and offers suggestions for stakeholders on how to bridge knowledge gaps and clinicians’ misgivings to bring this powerful approach to the clinic to improve care of the patients we serve.

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Fig. 1: Characteristics and applications of machine learning in cardiovascular medicine.

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Authors and Affiliations

  1. Carter Immunology Center, University of Virginia, Charlottesville, VA, USA

    Tanyaporn Pattarabanjird & Coleen McNamara

  2. Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA

    Tanyaporn Pattarabanjird & Coleen McNamara

  3. Department of Biomedical Engineering University of Virginia, Charlottesville, VA, USA

    Tanyaporn Pattarabanjird

  4. Division of Cardiovascular Medicine/Department of Medicine, University of Virginia, Charlottesville, VA, USA

    Coleen McNamara

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  1. Tanyaporn Pattarabanjird
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  2. Coleen McNamara
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Correspondence to Coleen McNamara.

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The authors declare no competing interests.

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Pattarabanjird, T., McNamara, C. The clinicians’ perspectives on machine learning. Nat Cardiovasc Res 1, 189–190 (2022). https://doi.org/10.1038/s44161-022-00033-9

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