The impact of machine learning on patient care: A systematic review

David Ben-Israel, W. Bradley Jacobs, Steve Casha, Stefan Lang, Won Hyung A. Ryu, Madeleine de Lotbiniere-Bassett, David W. Cadotte

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations

Abstract

Background: Despite the expanding use of machine learning (ML) in fields such as finance and marketing, its application in the daily practice of clinical medicine is almost non-existent. In this systematic review, we describe the various areas within clinical medicine that have applied the use of ML to improve patient care. Methods: A systematic review was performed in accordance with the PRISMA guidelines using Medline(R), EBM Reviews, Embase, Psych Info, and Cochrane Databases, focusing on human studies that used ML to directly address a clinical problem. Included studies were published from January 1, 2000 to May 1, 2018 and provided metrics on the performance of the utilized ML tool. Results: A total of 1909 unique publications were reviewed, with 378 retrospective articles and 8 prospective articles meeting inclusion criteria. Retrospective publications were found to be increasing in frequency, with 61 % of articles published within the last 4 years. Prospective articles comprised only 2 % of the articles meeting our inclusion criteria. These studies utilized a prospective cohort design with an average sample size of 531. Conclusion: The majority of literature describing the use of ML in clinical medicine is retrospective in nature and often outlines proof-of-concept approaches to impact patient care. We postulate that identifying and overcoming key translational barriers, including real-time access to clinical data, data security, physician approval of “black box” generated results, and performance evaluation will allow for a fundamental shift in medical practice, where specialized tools will aid the healthcare team in providing better patient care.

Original languageEnglish (US)
Article number101785
JournalArtificial Intelligence in Medicine
Volume103
DOIs
StatePublished - Mar 2020
Externally publishedYes

Keywords

  • Artificial intelligence
  • Clinical practice
  • Machine learning
  • Patient care
  • Systematic review

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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