Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment. Issue 1 (29th July 2021)

• Record Type: Journal Article
• Authors: Wilson, Anthony; Saeed, Haroon; Pringle, Catherine; Eleftheriou, Iliada; Bromiley, Paul A; Brass, Andy
• Is Part Of: BMJ health & care informatics. Volume 28:Issue 1(2021)
• Publication Date: 2021-07-29
• Subjects: health care sector -- medical informatics -- information science; Medical informatics -- Great Britain -- Periodicals; Information storage and retrieval systems -- Medical care -- Periodicals; Primary care (Medicine) -- Great Britain -- Data processing -- Periodicals; 362.10285
• DOI: 10.1136/bmjhci-2021-100323
• Languages: English
• ISSNs: 2632-1009

Abstract

Abstract : 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.