Science· 2019Q1
Adversarial attacks on medical machine learning
- 1,029citations
- Q1SCImago
- 2019year
Short summary
Machine learning models used in healthcare are vulnerable to adversarial attacks, where subtle input perturbations can cause misclassifications with potentially severe consequences.
AI-generated from the title and abstract; the full text is not read.
Key points
- Machine learning models in medicine can be fooled by adversarial attacks.
- Subtle changes to input data can cause significant misclassifications.
- These attacks pose a threat to patient safety and AI deployment in healthcare.
AI-generated from the title and abstract; the full text is not read.
Abstract
Emerging vulnerabilities demand new conversations
The authors' abstract, as published at the source. Science, 2019 · DOI ↗
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Field: Health Informatics
Health InformaticsMedicine