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State of the Art in Artificial Intelligence and Machine Learning Techniques for Improving Patient Outcomes Pertaining to the Cardiovascular and Respiratory Systems

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Cardiac Bioelectric Therapy

Abstract

With the ever-increasing amount of biomedical data gathered by hospital monitors, wearable devices, and the Internet of Things (IoT) and also with the advancement in artificial intelligence (AI) and computational hardware, there is an explosion of interest in applying AI in solving problems in biomedicine and healthcare. This chapter provides a basic introduction to AI and explains the motivation for using AI in medicine. The main body of this chapter summarizes the recent applications of AI to improve patients’ cardiovascular and respiratory system outcomes. At the end, the limitations and the forecasted future of AI in medicine are also discussed.

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Acknowledgments

The work was supported by a grant-in-aid (#15GRNT23070001) from the American Heart Association (AHA); the Ricbac Foundation; NIH grants 1 R01 HL135335-01, 1 R21 HL137870-01, and 1 R21EB026164-01; and a Founders Affiliate Postdoctoral Fellowship (#19POST34450149) from the AHA.

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Authors and Affiliations

  1. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Wan-Tai M. Au-Yeung, Rahul Kumar Sevakula & Antonis A. Armoundas

  2. The Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA

    Jagmeet P. Singh & E. Kevin Heist

  3. Healthcare Transformation Lab, Massachusetts General Hospital, Boston, MA, USA

    Eric M. Isselbacher

  4. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Antonis A. Armoundas

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  1. Wan-Tai M. Au-Yeung
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  2. Rahul Kumar Sevakula
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  3. Jagmeet P. Singh
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  4. E. Kevin Heist
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  5. Eric M. Isselbacher
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  6. Antonis A. Armoundas
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Correspondence to Antonis A. Armoundas .

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  1. Department of Biomedical Engineering, The George Washington University, Washington, DC, USA

    Igor R. Efimov

  2. National Heart & Lung Institute, Imperial College London, London, UK

    Fu Siong Ng

  3. Philips EPD Solutions, Cambridge, MA, USA

    Jacob I. Laughner

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Au-Yeung, WT.M., Sevakula, R.K., Singh, J.P., Heist, E.K., Isselbacher, E.M., Armoundas, A.A. (2021). State of the Art in Artificial Intelligence and Machine Learning Techniques for Improving Patient Outcomes Pertaining to the Cardiovascular and Respiratory Systems. In: Efimov, I.R., Ng, F.S., Laughner, J.I. (eds) Cardiac Bioelectric Therapy. Springer, Cham. https://doi.org/10.1007/978-3-030-63355-4_24

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