Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias.

Journal: British journal of anaesthesia
Published Date: Feb 1, 2022

Abstract

BACKGROUND: Artificial intelligence (AI) has the potential to personalise mechanical ventilation strategies for patients with respiratory failure. However, current methodological deficiencies could limit clinical impact. We identified common limitations and propose potential solutions to facilitate translation of AI to mechanical ventilation of patients.

Authors

  • Jack Gallifant
    Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK. Electronic address: jack.gallifant@kcl.ac.uk.
  • Joe Zhang
    Department of Adult Critical Care, Guy's and St Thomas' NHS Foundation Trust, King's Health Partners, London, UK; Institute of Global Health Innovation, Imperial College London, London, UK.
  • Maria Del Pilar Arias Lopez
    SATI-Q Program, Argentine Society of Intensive Care, Buenos Aires, Argentina.
  • Tingting Zhu
    Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.
  • Luigi Camporota
    Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK; Department of Adult Critical Care, Guy's and St Thomas' NHS Foundation Trust, King's Health Partners, London, UK.
  • Leo A Celi
    Beth Israel Deaconess Medical Center, Pulmonary Division and Harvard Medical School, Boston, MA 02215, USA.
  • Federico Formenti
    Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK; Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK; Department of Biomechanics, University of Nebraska Omaha, Omaha, NE, USA.

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