TY - JOUR
T1 - The impact of machine learning on patient care
T2 - A systematic review
AU - Ben-Israel, David
AU - Jacobs, W. Bradley
AU - Casha, Steve
AU - Lang, Stefan
AU - Ryu, Won Hyung A.
AU - de Lotbiniere-Bassett, Madeleine
AU - Cadotte, David W.
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/3
Y1 - 2020/3
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Clinical practice
KW - Machine learning
KW - Patient care
KW - Systematic review
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U2 - 10.1016/j.artmed.2019.101785
DO - 10.1016/j.artmed.2019.101785
M3 - Review article
C2 - 32143792
AN - SCOPUS:85077442177
SN - 0933-3657
VL - 103
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
M1 - 101785
ER -