Future of machine learning in paediatrics

Research output: Contribution to journalReview article (Academic Journal)peer-review

Abstract

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse and interpret extremely large amounts of data, which can then be applied to create predictive models. Such applications of this technology are now ubiquitous in our day-to-day lives: predictive text, spam filtering, and recommendation systems in social media, streaming video and e-commerce to name a few examples. It is only more recently that ML has started to be implemented against the vast amount of data generated in healthcare. The emerging role of AI in refining healthcare delivery was recently highlighted in the 'National Health Service Long Term Plan 2019'. In paediatrics, workforce challenges, rising healthcare attendance and increased patient complexity and comorbidity mean that demands on paediatric services are also growing. As healthcare moves into this digital age, this review considers the potential impact ML can have across all aspects of paediatric care from improving workforce efficiency and aiding clinical decision-making to precision medicine and drug development.

Original languageEnglish
JournalArchives of Disease in Childhood
Early online date22 Jul 2021
DOIs
Publication statusE-pub ahead of print - 22 Jul 2021

Bibliographical note

Publisher Copyright:
© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.

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