Machine learning in primary care: potential to improve public health.

Journal: Journal of medical engineering & technology
Published Date: Dec 7, 2020

Abstract

It is estimated that missed opportunities for diagnosis occur in 1 in 20 primary care appointments. This is not only detrimental to individual patients, but also to the healthcare system as health outcomes are affected and healthcare expenditure inevitably increases. There are many potential solutions to limit the number of missed opportunities for diagnosis and management, one of which is through the use of artificial intelligence. Artificial intelligence and machine learning research and capabilities have exponentially grown in the past decades, with their applications in medicine showing great promise. As such, this review aims to discuss the possible uses of machine learning in primary care to maximise the quality of care provided.

Authors

  • Jungwoo Kang
    Barts and the London Medical School, Queen Mary University of London, London, United Kingdom.
  • Moghees Hanif
    Barts and the London Medical School, Queen Mary University of London, London, United Kingdom.
  • Eushaa Mirza
    Barts and the London Medical School, Queen Mary University of London, London, United Kingdom.
  • Muhammad Asad Khan
    Barts and the London Medical School, Queen Mary University of London, London, United Kingdom.
  • Muzaffar Malik
    Department of Medical Education, Brighton and Sussex Medical School, University of Brighton, Brighton, United Kingdom.

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