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

Anthony Wilson; Haroon Saeed; Catherine Pringle; Iliada Eleftheriou; Paul A. Bromiley; Andy Brass

doi:10.1136/bmjhci-2021-100323

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

Anthony Wilson^*, Haroon Saeed, Catherine Pringle, Iliada Eleftheriou, Paul A. Bromiley, Andy Brass

^*Corresponding author for this work

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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.

Original language	English
Article number	e100323
Journal	BMJ Health and Care Informatics
Volume	28
Issue number	1
DOIs	https://doi.org/10.1136/bmjhci-2021-100323
Publication status	Published - 29 Jul 2021

Keywords

health care sector
information science
medical informatics

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e100323.fullFinal published version, 522 KBLicence: CC BY-NC

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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.",

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

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