In this section
@article{Rajput:2026:10.2196/93950,
author = {Rajput, K and Zuberi, S and Elhajj, M and Ochieng, W and Darzi, A and Ghafur, S},
doi = {10.2196/93950},
journal = {Journal of Medical Internet Research},
pages = {e93950--e93950},
title = {Mapping Machine Learning–Driven Cybersecurity Solutions in Health Care: Scoping Literature Review},
url = {http://dx.doi.org/10.2196/93950},
volume = {28},
year = {2026}
}
TY - JOUR
AB - <jats:title>Abstract</jats:title> <jats:sec sec-type="background"> <jats:title>Background</jats:title> <jats:p>Health care systems face escalating cyberattacks, including the UK Synnovis ransomware attack, which halted pathology services for 14 weeks; the Ascension Health breach affecting 5.6 million patients; and the Change Healthcare breach costing US $2.5 billion. Conventional cybersecurity measures in health care remain reactive and inadequate against evolving threats. Machine learning (ML) offers adaptive, predictive, real-time cyber defense; yet, there is limited clarity on how ML tools are applied across cybersecurity domains, their real-world effectiveness, and where gaps remain.</jats:p> </jats:sec> <jats:sec sec-type="objective"> <jats:title>Objective</jats:title> <jats:p>This study aims to map ML applications in health care cybersecurity against the National Institute of Standards and Technology Cybersecurity Framework version 2.0, summarize ML performance, and identify research gaps and implementation considerations.</jats:p> </jats:sec> <jats:sec sec-type="methods"> <jats:title>Methods</jats:title> <jats:p>A systematic search of Ovid MEDLINE, Embase, and Scopus was conducted on July 30, 2025, for studies between 2019 and 2025. Eligible studies applied ML-based approaches to organizational-level cybersecurity in health care settings, with outcomes related to data privacy or cybersecurity strengthening. Studies on smart devices, blockchain, or those lacking empirical data were excluded. Title and abstract and full-text screening were conducted independently by 2 (KR and SZ) reviewers following the Arksey and O’Malley f
AU - Rajput,K
AU - Zuberi,S
AU - Elhajj,M
AU - Ochieng,W
AU - Darzi,A
AU - Ghafur,S
DO - 10.2196/93950
EP - 93950
PY - 2026///
SP - 93950
TI - Mapping Machine Learning–Driven Cybersecurity Solutions in Health Care: Scoping Literature Review
T2 - Journal of Medical Internet Research
UR - http://dx.doi.org/10.2196/93950
UR - https://doi.org/10.2196/93950
VL - 28
ER -