Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making.

Journal: BMJ health & care informatics
Published Date:

Abstract

BACKGROUND: Predictive models have been used in clinical care for decades. They can determine the risk of a patient developing a particular condition or complication and inform the shared decision-making process. Developing artificial intelligence (AI) predictive models for use in clinical practice is challenging; even if they have good predictive performance, this does not guarantee that they will be used or enhance decision-making. We describe nine stages of developing and evaluating a predictive AI model, recognising the challenges that clinicians might face at each stage and providing practical tips to help manage them.

Authors

  • Nehal Hassan
    School of Pharmacy, Newcastle University, King George VI Building, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: n.a.m.hassan2@newcastle.ac.uk.
  • Robert Slight
    Newcastle Upon Tyne Hospitals NHS Foundation Trust, Freeman Hospital, High Heaton, Newcastle upon Tyne, NE7 7DN, UK. Electronic address: bob.slight@nhs.net.
  • Graham Morgan
  • David W Bates
    Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
  • Suzy Gallier
    PIONEER Health Data Research Hub, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • Elizabeth Sapey
    University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • Sarah Slight
    School of Pharmacy, Newcastle University School of Pharmacy, Newcastle Upon Tyne, UK Sarah.Slight@newcastle.ac.uk.