The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Adversarial attacks on medical machine learning
Open accessAbstract
Emerging vulnerabilities demand new conversations
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
End-To-End Arguments In System Design
And, S. R., Saltzer, J. H., Reed, D. P., & Clark, D. D. (1984) · Cited by 2,130
Wild patterns: Ten years after the rise of adversarial machine learning
Biggio, B., & Roli, F. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.023 · Cited by 1,563
Robustness May Be at Odds with AccuracyPreprint
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., & Mądry, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12152 · Cited by 1,513
The Future of the Internet--And How to Stop It
Zittrain, J. L. (2008) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 1,371
Self-citation(2017) · doi:10.48550/arxiv.1712.02779 · Cited by 300
Adversarial Attacks Against Medical Deep Learning SystemsPreprint
Finlayson, S. G., Chung, H. W., Kohane, I. S., & Beam, A. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.05296 · Cited by 289
Self-citationPhysician manipulation of reimbursement rules for patients: between a rock and a hard place.
Wynia, M. K., Cummins, D. S., VanGeest, J. B., & Wilson, I. B. (2000) · PubMed · doi:10.1001/jama.283.14.1858 · Cited by 238
Corruption: medicine's dirty open secret
Jain, A., Nundy, S., & Abbasi, K. A. (2014) · BMJ · doi:10.1136/bmj.g4184 · Cited by 69
Overbilling vs. Downcoding — The Battle between Physicians and Insurers
Kesselheim, A. S., & Brennan, T. A. (2005) · New England Journal of Medicine · doi:10.1056/nejmp058011 · Cited by 26
Flagged first, then by citations
Research works printed but not linked: 4
- N. Dalvi ., in KDD '04: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2004).
- J. Li S. Ji T. Du B. Li T. Wang arXiv:1812.05271 [cs.CR] (13 December 2018). · arXiv
- A. Jain, S. Nundy, K. Abbasi, BMJ 348, g4181 (2014).
- 10.1145/357401.357402 · DOI
Cited by
Sorted by citations
Items 801–900 of 1,001
Lofaro, A. (2019) · IEEE International Conference on Application of Information and Communication Technologies (AICT) · doi:10.1109/aict47866.2019.8981798
On the Matrix-Free Generation of Adversarial Perturbations for Black-Box AttacksPreprint
Shibata, H., Hanaoka, S., Nomura, Y., Hayashi, N., & Abe, O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07317
Buchlak, Q. D., Farrokhi, F. R., Sikora, M., Esmaili, N., Marsans, M., McLeod, P. S., Mark, J., Cox, E. J., Bennett, C. C., & Carlson, J. D. (2020) · World Neurosurgery · doi:10.1016/j.wneu.2020.03.016
Quantifying the Uncertainty of Precision Estimates for Rule based Text ClassifiersPreprint
Nutaro, J. J., & Özmen, Ö. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.09198
Liu, Y., Yang, L., Phene, S., & Peng, L. H. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00013-2
On Recent Security Issues in Machine Learning
Alani, M. M. (2020) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom50211.2020.9238337
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing AttackPreprint
Liu, A., Li, B., Li, T., Zhou, P., & Wang, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.03482
Training or Architecture? How to Incorporate Invariance in Neural NetworksPreprint
Gandikota, K. V., Geiping, J., Lähner, Z., Czapliński, A., & Moeller, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.10044
Sampling Strategies for Compressive Imaging
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.020
An Introduction to Conventional Compressed Sensing
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.008
Techniques for Enhancing Performance
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.006
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.022
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.015
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.034
Compressed Sensing, Optimization and Wavelets
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.007
The Essentials of Compressive Imaging
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.003
Neural Circuit Mechanisms Of Stimulus Selection Underlying Spatial Attention
Mahajan, N. R. (2020)
Artificial Intelligence and Healthcare Ethics
Rana, A., Goedmakers, C. M. W., & Smith, T. R. (2021) · Springer eBooks · doi:10.1007/978-3-030-78075-3_31
Hossain, K. F., Kamran, S. A., Ma, X., & Tavakkoli, A. (2021) · arXiv (Cornell University)
Local Structure and Nonuniform Recovery
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.016
Robust, Deep, and Reinforcement Learning for Management of Communication and Power NetworksPreprint
Sadeghi, A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.05395
Recovery Guarantees for Wavelet-Based Compressive Imaging
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.021
Data to Information: Computational Models and Analytic Methods
Visweswaran, S., & Tajgardoon, M. (2021) · Health informatics · doi:10.1007/978-3-030-70558-9_10
Stable and Accurate Neural Networks for Compressive Imaging
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.027
A Short Guide to Compressive Imaging
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.005
Infinite-Dimensional Compressed Sensing
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.018
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.029
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.001
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.030
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.002
Images, Transforms and Sampling
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.004
Optimization for Compressed Sensing
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.010
(2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.038
Properties of Walsh Functions and the Walsh Transform
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.035
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.036
A Taste of Wavelet Approximation Theory
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.013
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.037
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.012
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.039
Analysis of Optimization Algorithms
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.011
Accuracy and Stability of Deep Learning for Compressive Imaging
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.026
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.032
Local Structure and Uniform Recovery
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.017
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.028
Compressed Sensing with Local Structure
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.014
Deep Learning for Compressive Imaging
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.025
Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.031
Towards robust inference against distribution shifts in computer vision
Tang, K. (2021) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/154119
Adversarially robust subspace learning in the spiked covariance model
Sha, F., & Zhang, R. (2022) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11580
Aprendizaje automático aplicado en área de la salud. Parte 2
Quiroz, N. H., Posadas‐Martínez, M. L., Rossi, E., Giunta, D. H., & Risk, M. R. (2022) · Revista del Hospital Italiano de Buenos Aires · doi:10.51987/revhospitalbaires.v42i1.152
Gupta, A., & Gupta, N. (2022) · Journal of Anaesthesiology Clinical Pharmacology · doi:10.4103/joacp.joacp_619_20
Automated Phase Segmentation and Quantification of High-Resolution Tem Image for Alloy DesignPreprint
Liu, S., Amin-Ahmadi, B., Liu, R., Zheng, Q., & Zhang, X. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4101126
Security- and privacy-preserving ML/DL paradigms for 5G communication technology in smart healthcare
Negi, P. S., Garg, A. K., & Lal, R. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90615-9.00010-4
Huang, Z., Li, Y., Cheng, H., Li, G., & Liang, Z. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4097411
Musil, B., & Plohl, N. (2022) · doi:10.18690/um.ff.11.2022.11 · Cited by 1
Umetna inteligenca in eksistenčno tveganje
Lipuš, A. (2022) · doi:10.18690/um.ff.11.2022.10
Borstner, B., & Šetar, N. (2022) · doi:10.18690/um.ff.11.2022.5
Transformacija 'inteligence roja' {Swarm Intelligence} na umetno inteligenco
Martinc, U., Aberšek, B., & Borstner, B. (2022) · doi:10.18690/um.ff.11.2022.9
Weighted Adaptive Perturbations Adversarial Training for Improving Robustness
Wang, Y., Zhang, D., & Zhang, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20865-2_30
Model Fooling Threats Against Medical Imaging
Sipola, T., Kokkonen, T., & Karjalainen, M. (2022) · Artificial Intelligence and Cybersecurity · doi:10.1007/978-3-031-15030-2_13
Li, F., Lai, L., & Cui, S. R. (2022) · Wireless networks · doi:10.1007/978-3-031-16375-3_1
Ethical and Legal Risks of Artificial Intelligence in Radiology
D’Antonoli, T. A. (2022) · Springer eBooks · doi:10.1007/978-3-030-99680-2_12
Learning while Respecting Privacy and Robustness to Adversarial Distributed Datasets
Sadeghi, A., & Giannakis, G. B. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909977
A Suite of Incremental Image Degradation Operators for Testing Image Classification Algorithms
Swingler, K. M. (2022) · International Joint Conference on Computational Intelligence · doi:10.5220/0011511000003332
Robust sparsified deep learning
Jian, T. (2022) · doi:10.17760/d20467274
Thesing, L., & Hansen, A. C. (2023) · PAMM · doi:10.1002/pamm.202200174
Nogueira, M. A., & Bijnens, B. H. (2023) · Springer eBooks · doi:10.1007/978-3-031-05071-8_8
Graph-Based Methods Coupled with Specific Distributional Distances for Adversarial Attack DetectionPreprint
Nwaigwe, D., Carboni, L., Mermillod, M., Achard, S., & Dojat, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4474516
Deep Learning Techniques for Medical Image Segmentation and Object Recognition
Kim, K. C., Jang, T. J., & Seo, J. K. (2023) · Mathematics in industry · doi:10.1007/978-981-99-1839-3_2
Durnyak, B., Neroda, T. V., Shepita, P., Tupychak, L., Pasieka, N., & Romanyshyn, Y. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-36115-9_19 · Cited by 1
Mathew, S., Durga, S. N., & Chowdhary, C. L. (2023) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.163.09
MultiVT: Multiple-Task Framework for Dentistry
Rella, E. M., Chhatkuli, A., Konukoğlu, E., & Gool, L. V. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45857-6_2
The consolidation of the digital technological system
Neto, V. J. D. S. (2022) · doi:10.47749/t/unicamp.2022.1253623
Adversarial examples detection based on ensembles of defenses and counter-attacks
Sánchez, R. A. V. R. (2022) · doi:10.47749/t/unicamp.2022.1245035
Transient Deception: Recent Advances and Trends in Short-Lived Adversarial ExamplesPreprint
Srivastav, D. (2023) · Research Square · doi:10.21203/rs.3.rs-3611507/v1
Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant AttacksPreprint
Biswas, A., Nasim, M. A. A., Gupta, K. D., George, R., Shujaee, K. A., & Rashid, A. (2024) · Research Square · doi:10.21203/rs.3.rs-3924726/v1 · Cited by 6
Vineetha, B., Suryaprasad, J., Shylaja, S. S., & Honnavalli, P. B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-7569-3_28 · Cited by 1
Cyberattaques contre les systèmes d’information de santé au Canada
Harish, V., Ackery, A., Grant, K. L., Jamieson, T., & Mehta, S. (2024) · Canadian Medical Association Journal · doi:10.1503/cmaj.230436-f
Wang, L., & Zhao, J. (2024) · Studies in big data · doi:10.1007/978-3-031-55885-6_9
Perturbation Augmentation for Adversarial Training with Diverse Attacks
Serbes, D., & Baytaş, İ. M. (2024) · Gazi University Journal of Science Part A Engineering and Innovation · doi:10.54287/gujsa.1458880
Analysis of aortic rupture: A computational biomechanics perspective
Liu, M., Sun, W., Elefteriades, J. A., & Liang, L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95484-6.00007-5
Su, W., Sun, J., & Bian, J. (2024) · International Symposium on Computer Applications and Information Systems (ISCAIS) · doi:10.1117/12.3035020
Can AI Distort Human Capital? *Preprint
Li, X., & Zhou, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4929303
Nowhere to Hide: Toward Robust Reactive Medical Adversarial Defense
Yao, Q., He, Z., Yu, X., & Zhou, S. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635644
Pham, V. T., Ha, M. H., Bui, B. V. Q., & Hy, T. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.28.615584
AI in biomarkers analysis: new horizons for personalized medicine
Krylov, A. P. (2024) · Terapevt (General Physician) · doi:10.33920/med-12-2407-02
Ayubcha, C., Sajed, S., Omara, C., Lebouille-Veldman, A. B., Singh, S. B., Lokesha, Y. U., Liu, A. X., Aziz‐Sultan, M. A., Smith, T. R., & Beam, A. L. (2024) · medRxiv · doi:10.1101/2024.10.12.24315391
Self-citationSravanthi, C., & Kumar, B. S. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-8043-3_145
Guo, D., & Choo, K. R. (2024) · doi:10.2196/preprints.68291
Beydemir, A., Dedeoğulları, E. S., Arik, Z. B., Özdemir, E., & Huri, G. (2024) · Springer eBooks · doi:10.1007/978-3-031-70310-2_4
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
