Mapping Machine Learning–Driven Cybersecurity Solutions in Health Care: Scoping Literature Review
Background: 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
Record details
Published: 27 July 2026
Source: JMIR (Journal of Medical Internet Research)
Category: Research
Topics: Healthcare · Military & security
Retrieved: 28 July 2026
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How to cite this record
ethics.ai (27 July 2026), “Mapping Machine Learning–Driven Cybersecurity Solutions in Health Care: Scoping Literature Review,” evidence record 13878, https://ethics.ai/record/13878 (originally published by JMIR (Journal of Medical Internet Research)).
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