Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment

Research output: Contribution to journalArticlepeer-review

  • External authors:
  • Anthony Wilson
  • Haroon Saeed
  • Catherine Pringle
  • Andy Brass


There is much discussion concerning 'digital transformation' in healthcare and the potential of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions deployed in routine healthcare settings. This is in part due to the numerous challenges inherent in delivering an AI project in a clinical environment. In this article, several UK healthcare professionals and academics reflect on the challenges they have faced in building AI solutions using routinely collected healthcare data.These personal reflections are summarised as 10 practical tips. In our experience, these are essential considerations for an AI healthcare project to succeed. They are organised into four phases: conceptualisation, data management, AI application and clinical deployment. There is a focus on conceptualisation, reflecting our view that initial set-up is vital to success. We hope that our personal experiences will provide useful insights to others looking to improve patient care through optimal data use.

Bibliographical metadata

Original languageEnglish
Article numbere100323
JournalBMJ Health and Care Informatics
Issue number1
Publication statusPublished - 29 Jul 2021