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Adversarial attacks on medical machine learning
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Emerging vulnerabilities demand new conversations
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“Existing research has shown that even small, imperceptible variations in the input data might lead AI models into incorrect decisions.60”
From FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2025) · cites this paper as [60]
The cited source says what the claim attributes to it.
In the citing paper
“changes to medical imaging that are invisible, or appear random, to the human eye may change the likelihood of the diagnostic output entirely55,56.”
From Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension (2020) · cites this paper as [56]
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“For example, changes to medical imaging that are invisible, or appear random, to the human eye may change the likelihood of the resultant diagnostic output entirely47,48.”
From Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension (2020) · cites this paper as [48]
In the citing paper
“For example, Finlayson et al. [110] have shown in a recent publication that the system’s output can completely be changed so that it classifies a mole as malignant with 100% confidence by making a small change in how inputs are presented to the system [109].”
From Ethical and legal challenges of artificial intelligence-driven healthcare (2020) · cites this paper as [110]
In the citing paper
“There is always a relatively high risk of adversarial attacks on health data systems [155]”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [155]
In the citing paper
“Moreover, such attacks have been already realized on ML/DL-based medical systems [31].”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [31]
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Solving electrical impedance tomography with deep learning
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Clinical implementation of AI technologies will require interpretable AI models
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Experimental quantum adversarial learning with programmable superconducting qubits
Ren, W., Li, W., Xu, S., Wang, K., Jiang, W., Jin, F., Zhu, X., Chen, J., Song, Z., Zhang, P., et al. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00351-9 · Cited by 125
Generating synthetic data for medical imaging
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Noninterpretive Uses of Artificial Intelligence in Radiology
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Quantum adversarial machine learning
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Metadata
- Authors
- Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L., Beam, A. L., & Kohane, I. S.
- Affiliations
- Harvard University (US)
- MIT Media Lab (US)
- Venue
- Science
- Published
- 2019-03-22
- Volume
- 363
- Issue
- 6433
- Pages
- 1287-1289
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7657648
- OpenAlex
- W2924551358
- Semantic Scholar
- e718828e8f776d9f80daa3f8e0af6895f5d34c44
- Cited by
- 1,088
- References
- 10
- Citations by year
- 2026: 162 · 2025: 155 · 2024: 123 · 2023: 153 · 2022: 178 · 2021: 166 · 2020: 107 · 2019: 43
- FWCI
- 56.32
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (T32GM007753)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- adversarial attacks, machine learning in medicine, model vulnerability, robustness, clinical decision support, cybersecurity in healthcare
- MeSH terms
- Machine Learning, Fraud, Humans, Insurance Claim Review
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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