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.
- healthcare economics and organisations
- information technology
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SLC and KP contributed equally.
Contributors AVR designed the review. AVR and MAS supervised this review. SLNC and KP undertook the literature searches and wrote the manuscript. All authors provided input in the manuscript revisions and agreed on the final draft.
Funding KP is an academic clinical fellow funded by the National Institute for Health Research (ACF-2019-25-012). SLNC is funded by the Wellcome Trust (203918/Z/16/Z). AVR and SLNC are also part of the MRC-funded CLUSTER consortium (MR/R013926/1). MAS receives MRC Precision Medicine award (MRC/R013942/1) and MRC Global Challenges award (MR/P024297/1) funding. This research was funded in whole, or in part, by the Wellcome Trust (203918/Z/16/Z).
Disclaimer The funders had no input in the content of this review.
Competing interests None declared.
Provenance and peer review Commissioned; internally peer reviewed.