Evidence record 13878 · automatically gathered

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

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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)).

JSON

Use and limitations

This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.