Adversarial attacks on medical machine learning

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

Untitled work

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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

Physician 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

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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

Vulnerability of Large Language Models to Prompt Injection When Providing Medical Advice

Lee, R. W., Jun, T. J., Lee, J.-M., Cho, S. I., Park, H., & Suh, J. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.49963 · Cited by 53

Anatomical context protects deep learning from adversarial perturbations in medical imaging

Li, Y., Zhang, H., Bermudez, C. L., Chen, Y., Landman, B. A., & Vorobeychik, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.085 · Cited by 41

“Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks

Liu, J., Daniel Capurro, N., Nguyen, A. N., & Verspoor, K. M. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104149 · Cited by 42

Source-Free Domain Adaptation for Privacy-Preserving Seizure Prediction

Zhao, Y., Feng, S., Li, C., Song, R., Deng, L., & Chen, X. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3297323 · Cited by 39

A Novel Video Coding Strategy in HEVC for Object Detection

Cai, Q., Chen, Z., Wu, D. O., Liu, S., & Li, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3056134 · Cited by 39

ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks

Hossain, K. F., Kamran, S. A., Tavakkoli, A., Pan, L., Ma, X., Rajasegarar, S., & Karmaker, C. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00016 · Cited by 39

eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics

Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., & Mongelli, M. (2022) · IEEE Access · doi:10.1109/access.2022.3197299 · Cited by 39

Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions

Naresh, V. S., & Thamarai, M. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1490 · Cited by 45

Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images

Sadre, R., Ophus, C., Butko, A., & Weber, G. H. (2021) · Microscopy and Microanalysis · doi:10.1017/s1431927621000167 · Cited by 40

Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey

Swain, S., Bhushan, B., Dhiman, G., & Viriyasitavat, W. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09733-8 · Cited by 40

Artifical intelligence use in orthopedics: an ethical point of view

Biçer, E. K., Fangerau, H., & Sur, H. (2023) · EFORT Open Reviews · doi:10.1530/eor-23-0083 · Cited by 37

Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging

Szabó, L. E., Raisi‐Estabragh, Z., Salih, A. M., McCracken, C., Pujadas, E. R., Gkontra, P., Kiss, M., Maurovich‐Horvat, P., Vágó, H., Merkely, B. P., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1016032 · Cited by 42

Measurements, Algorithms, and Presentations of Reality: Framing Interactions with AI-Enabled Decision Support

van Berkel, N., Bellio, M., Skov, M. B., & BLANDFORD, A. E. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3571815 · Cited by 42

A Discriminative Level Set Method with Deep Supervision for Breast Tumor Segmentation

Hussain, S., Xi, X., Ullah, I., Inam, S. A., Naz, F., Shaheed, K., Ali, S. A., & Tian, C. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105995 · Cited by 36

Speciesism and Preference of Human–Artificial Intelligence Interaction: A Study on Medical Artificial Intelligence

Huo, W., Zhang, Z., Qu, J., Yan, J., Yan, S., Yan, J., & Shi, B. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2176985 · Cited by 41

The Troublesome Kernel: On Hallucinations, No Free Lunches, and the Accuracy-Stability Tradeoff in Inverse Problems

Gottschling, N. M., Antun, V., Hansen, A. C., & Adcock, B. (2025) · SIAM Review · doi:10.1137/23m1568739 · Cited by 131

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

Kulkarni, V., Gawali, M., & Kharat, A. T. (2021) · JMIR Medical Informatics · doi:10.2196/28776 · Cited by 36

Adversarial attacks and defenses in physiological computing: a systematic review

Wu, D., Xu, J., Fang, W., Zhang, Y., Yang, L., Xu, X., Luo, H., & Yu, X. M. (2022) · National Science Open · doi:10.1360/nso/20220023 · Cited by 43

Adversarial NLP for Social Network Applications: Attacks, Defenses, and Research Directions

Alsmadi, I. M., Ahmad, K., Nazzal, M., Alam, F., Al‐Fuqaha, A., Khreishah, A. A., & Algosaibi, A. A. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3218743 · Cited by 64

Artificial intelligence for neurodegenerative experimental models

Marzi, S. J., Schilder, B. M., Nott, A., Frigerio, C. S., Willaime‐Morawek, S., Bucholc, M., Hanger, D. P., James, C. R., Lewis, P. A., Lourida, I., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13479 · Cited by 35

Robustness evaluation of deep neural networks for endoscopic image analysis: Insights and strategies

Jaspers, T. J. M., Boers, T. G. W., Kusters, C. H. J., Jong, M. R., Jukema, J. B., de Groof, A. J., Bergman, J. J. G. H. M., de With, P. H. N., & van der Sommen, F. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103157 · Cited by 42

12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context

Doyen, S., & Dadario, N. B. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.765406 · Cited by 37

MEFF – A model ensemble feature fusion approach for tackling adversarial attacks in medical imaging

Alzubaidi, L., Al-Dulaimi, K., hussain, H. A., Saihood, A., Fadhel, M. A., Jebur, S. A., Chen, Y., Albahri, A. S., Santamaría, J., Gupta, A., et al. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200355 · Cited by 34

Adversarial Attacks in Explainable Machine Learning: A Survey of Threats Against Models and Humans

Vadillo, J., Santana, R., & Lozano, J. A. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1567 · Cited by 42

Injecting and removing suspicious features in breast imaging with CycleGAN: A pilot study of automated adversarial attacks using neural networks on small images

Becker, A. S., Jendele, L., Skopek, O., Berger, N., Ghafoor, S., Marcon, M., & Konukoğlu, E. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108649 · Cited by 38

Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists

Amorim, J. P., Abreu, P. H., Fernández, A., Reyes, M., Santos, J., & Abreu, M. H. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3131358 · Cited by 33

Cybersecurity of Critical Infrastructure

Viganò, E., Loi, M., & Yaghmaei, E. (2020) · The International library of ethics, law and technology · doi:10.1007/978-3-030-29053-5_8 · Cited by 39

What machine learning can do for developmental biology

Villoutreix, P. (2021) · Development · doi:10.1242/dev.188474 · Cited by 35

Challenges of developing artificial intelligence‐assisted tools for clinical medicine

Shung, D. L., & Sung, J. J. �. (2021) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15378 · Cited by 33

Universal Adversarial Examples and Perturbations for Quantum Classifiers

Gong, W., & Deng, D.-L. (2021) · National Science Review · doi:10.1093/nsr/nwab130 · Cited by 39

Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches

Khaleel, Y. L., Habeeb, M. A., & Alnabulsi, H. (2024) · Applied Data Science and Analysis · doi:10.58496/adsa/2024/011 · Cited by 38

Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when?

Hoogenboom, S. A. M., Bağcı, U., & Wallace, M. B. (2019) · Techniques and Innovations in Gastrointestinal Endoscopy · doi:10.1016/j.tgie.2019.150634 · Cited by 35

Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples

Tuna, Ö. F., Çatak, F. Ö., & Eskil, M. T. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12132-7 · Cited by 34

Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer

Benning, L., Peintner, A., & Peintner, L. (2022) · Cancers · doi:10.3390/cancers14030623 · Cited by 34

Adversarial Deep Learning for Indoor Localization With Channel State Information Tensors

Wang, X., Wang, X., Mao, S., Zhang, J., Periaswamy, S. C., & Patton, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3155562 · Cited by 33

On the Adversarial Robustness of LASSO Based Feature Selection

Li, F., Lai, L., & Cui, S. R. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3115943 · Cited by 31

The Global Politics of Artificial Intelligence

Tinnirello, M. (2022) · Chapman & Hall/CRC eBooks · doi:10.1201/9780429446726 · Cited by 34

Physics‐Aware Machine Learning and Adversarial Attack in Complex‐Valued Reconfigurable Diffractive All‐Optical Neural Network

Chen, R., Li, Y., Lou, M., Fan, J., Tang, Y., Sensale‐Rodriguez, B., Yu, C., & Gao, W. (2022) · Laser & Photonics Review · doi:10.1002/lpor.202200348 · Cited by 37

Artificial Intelligence in Health Care: Insights From an Educational Forum

Barbour, A. B., Frush, J. M., Gatta, L. A., McManigle, W. C., Keah, N. M., Bejarano-Pineda, L., & Guerrero, E. M. (2019) · Journal of Medical Education and Curricular Development · doi:10.1177/2382120519889348 · Cited by 30

Requirements for Trustworthy Artificial Intelligence and its Application in Healthcare

Kim, M., Sohn, H., Choi, S. K., & Kim, S. (2023) · Healthcare Informatics Research · doi:10.4258/hir.2023.29.4.315 · Cited by 33

Application of machine learning based genome sequence analysis in pathogen identification

Gao, Y., & Liu, M. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1474078 · Cited by 37

Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review

Park, S. H., Kim, Y., Lee, J. Y., Yoo, S., & Kim, C. J. (2019) · Science Editing · doi:10.6087/kcse.164 · Cited by 38

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Bao, X.-L., Sun, Y., Zhan, X., & Li, G. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.1069248 · Cited by 30

Can AI pass the written European Board Examination in Neurological Surgery? - Ethical and practical issues

Stengel, F. C., Stienen, M. N., Ivanov, M., Gandía-González, M. L., Raffa, G., Ganau, M., Whitfield, P. C., & Motov, S. (2024) · Brain and Spine · doi:10.1016/j.bas.2024.102765 · Cited by 29

Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement

Navarrete‐Dechent, C., Liopyris, K., & Marchetti, M. A. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.06.040 · Cited by 29

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

Lim, J. S., Hong, M., Lam, W. S. T., Zhang, Z., Teo, Z. L., Liu, Y., Ng, W. Y., Foo, L. L., & Ting, D. S. W. (2022) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000846 · Cited by 31

Uncertainty Assessment for Deep Learning Radiotherapy Applications

van den Berg, C. A. T., & Meliadò, E. F. (2022) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2022.06.001 · Cited by 34

Improving adversarial robustness of medical imaging systems via adding global attention noise

Dai, Y., Qian, Y., Fang, L., Wang, B., Gu, Z., Wang, W., Wan, J., & Zhang, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107251 · Cited by 30

Artificial Intelligence for Cardiac Imaging-Genetics Research

de Marvao, A., Dawes, T. J. W., & O’Regan, D. P. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2019.00195 · Cited by 31

Quantum capsule networks

Liu, Z., Shen, P.-X., Li, W., Duan, L.-M., & Deng, D.-L. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/aca55d · Cited by 29

The risks of artificial intelligence: A narrative review and ethical reflection from an Oral Medicine group

Feng, Q. J., Harte, M., Carey, B., Alqarni, A., Monteiro, L. S., Freitas, M. D., Fricain, J., Lodi, G., Brailo, V., Andreoletti, M., et al. (2024) · Oral Diseases · doi:10.1111/odi.15100 · Cited by 30

Generative adversarial networks enhanced location privacy in 5G networks

Qu, Y., Zhang, J., Li, R., Zhang, X., Zhai, X., & Yu, S. (2020) · Science China Information Sciences · doi:10.1007/s11432-019-2834-x · Cited by 31

Target attack on biomedical image segmentation model based on multi-scale gradients

Shao, M., Zhang, G., Zuo, W., & Meng, D. (2020) · Information Sciences · doi:10.1016/j.ins.2020.12.013 · Cited by 28

Randomised controlled trials in medical AI: ethical considerations

Grote, T. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-107166 · Cited by 32

Limitations of Deep Learning for Inverse Problems on Digital Hardware

Boche, H., Fono, A., & Gitta Kutyniok, B. (2023) · IEEE Transactions on Information Theory · doi:10.1109/tit.2023.3326879 · Cited by 35

Responsible innovation in neurotechnology enterprises

Garden, H., Winickoff, D. E., Frahm, N., & Pfotenhauer, S. M. (2019) · OECD science, technology and industry working papers · doi:10.1787/9685e4fd-en · Cited by 34

Teaching cross-cultural design thinking for healthcare

Ferreira, M. F., Savoy, J. N., & Markey, M. K. (2020) · The Breast · doi:10.1016/j.breast.2019.12.015 · Cited by 29

Simple iterative method for generating targeted universal adversarial perturbations

Hirano, H., 29507, Takemoto, K., 24877, 40512356, 35270356700, & 100000509 (2020) · Institutional Repositories DataBase (IRDB) · Cited by 27

AI Challenges for Society and Ethics

Whittlestone, J., & Clarke, S. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197579329.013.3 · Cited by 35

Simple Iterative Method for Generating Targeted Universal Adversarial Perturbations

Hirano, H., & Takemoto, K. (2020) · Algorithms · doi:10.3390/a13110268 · Cited by 27

Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: a systematic review

Zeng, A., Houssami, N., Noguchi, N., Nickel, B., & Marinovich, M. L. (2024) · Breast Cancer Research and Treatment · doi:10.1007/s10549-024-07353-3 · Cited by 28

Adversarial artificial intelligence in radiology: Attacks, defenses, and future considerations

Dietrich, N. T., Gong, B., & Patlas, M. N. (2025) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2025.05.006 · Cited by 28

LinkBreaker: Breaking the Backdoor-Trigger Link in DNNs via Neurons Consistency Check

Chen, Z., Wang, S., Fu, A., Gao, Y., Yu, S., & Deng, R. H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3175616 · Cited by 27

Adversarial Artificial Intelligence in Insurance: From an Example to Some Potential Remedies

Ameridad, B., Cattaneo, M., Kenett, R. S., & Luciano, E. (2023) · Risks · doi:10.3390/risks11010020 · Cited by 29

A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease

Ramos, M. J., Kendall, T. J., Drozdov, I. A., & Fallowfield, J. A. (2023) · Annals of Hepatology · doi:10.1016/j.aohep.2023.101278 · Cited by 26

Power and reproducibility in the external validation of brain-phenotype predictions

Rosenblatt, M., Tejavibulya, L., Sun, H., Camp, C., Khaitova, M., Adkinson, B. D., Jiang, R., Westwater, M. L., Noble, S., & Scheinost, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01931-7 · Cited by 26

Machine learning robustness: a primer

Braiek, H. B., & Khomh, F. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323761-4.00012-2 · Cited by 52

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition

Franco, D., Oneto, L., Navarin, N., & Anguita, D. (2021) · Entropy · doi:10.3390/e23081047 · Cited by 28

Adversarial training for prostate cancer classification using magnetic resonance imaging

Hu, L., Zhou, D., Guo, X., Xu, W., Wei, L., & Zhao, J. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-1089 · Cited by 25

Outcomes of Adversarial Attacks on Deep Learning Models for Ophthalmology Imaging Domains

Yoo, T. K., & Choi, J. Y. (2020) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2020.3442 · Cited by 24

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

Murphy, M. P., & Brown, N. M. (2021) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000001679 · Cited by 25

What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured Data

Hu, Y., Chapman, A. P., Wen, G., & Hall, D. W. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3510030 · Cited by 28

Natural Images Allow Universal Adversarial Attacks on Medical Image Classification Using Deep Neural Networks with Transfer Learning

Minagi, A., Hirano, H., & Takemoto, K. (2022) · Journal of Imaging · doi:10.3390/jimaging8020038 · Cited by 24

Stabilizing deep tomographic reconstruction: Part B. Convergence analysis and adversarial attacks

Wu, W., Hu, D., Cong, W., Shan, H., Wang, S., Niu, C., Yan, P., Yu, H., Vardhanabhuti, V., & Wang, G. (2022) · Patterns · doi:10.1016/j.patter.2022.100475 · Cited by 24

Black Swan Events and Intelligent Automation for Routine Safety Surveillance

Kjoersvik, O., & Bate, A. (2022) · Drug Safety · doi:10.1007/s40264-022-01169-0 · Cited by 24

Artificial Intelligence and Infectious Disease Imaging

Chu, W. T., Reza, S. M. S., Anibal, J., Landa, A., Crozier, I., Bağcı, U., Wood, B. J., & Solomon, J. M. (2023) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiad158 · Cited by 26

Big Data in Medicine, the Present and Hopefully the Future

Riba, M., Sala, C. F., Toniolo, D., & Tonon, G. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00263 · Cited by 25

Privacy-preserving deep learning in medical informatics: applications, challenges, and solutions

Naresh, V. S., Thamarai, M., & Allavarpu, V. V. L. D. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10556-7 · Cited by 25

Analyzing Ghana's Pharmacy Act, 1994 (Act 489) Regarding Quality Control and Negligence Liability Measures for Artificial Intelligence Pharmacy Systems

Mensah, G., Mijwil, M. M., & Adamopoulos, I. (2024) · Babylonian Journal of Artificial Intelligence · doi:10.58496/bjai/2024/003 · Cited by 23

Adversarial radiomics: the rising of potential risks in medical imaging from adversarial learning

Barucci, A., & Neri, E. (2020) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-04879-8 · Cited by 28

A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks

Yao, Q., He, Z., Lin, Y., Ma, K., Zheng, Y., & Zhou, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_4 · Cited by 25

Deceptive Tricks in Artificial Intelligence: Adversarial Attacks in Ophthalmology

Zbrzezny, A. M., & Grzybowski, A. E. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12093266 · Cited by 23

Can stable and accurate neural networks be computed? - On the barriers of deep learning and Smale's 18th problem.Preprint

Antun, V., Colbrook, M. J., & Hansen, A. C. (2021) · arXiv (Cornell University) · Cited by 23

Genetic Information Insecurity as State of the Art

Schumacher, G. J., Sawaya, S. M., King, D., & Hansen, A. J. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.591980 · Cited by 22

Cyclical Adversarial Attack Pierces Black-box Deep Neural Networks

Huang, L., Wei, S., Gao, C., & Liu, N. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108831 · Cited by 22

A Feature Space-Restricted Attention Attack on Medical Deep Learning Systems

Wang, Z., Shu, X., Wang, Y., Feng, Y., Zhang, L., & Zhang, Y. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3209175 · Cited by 22

ChatGPT as an aid for pathological diagnosis of cancer

Malik, S., & Zaheer, S. (2023) · Pathology - Research and Practice · doi:10.1016/j.prp.2023.154989 · Cited by 22

Adversarial Attack Detection Using Explainable AI and Generative Models in Real-Time Financial Fraud Monitoring Systems

James, U. U., Idika, C. N., Enyejo, L. A., Abiodun, K. E., & Enyejo, J. O. (2024) · International Journal of Scientific Research and Modern Technology. · doi:10.38124/ijsrmt.v3i12.644 · Cited by 32

Risk Susceptibility of Brain Tumor Classification to Adversarial Attacks

Kotia, J., Kotwal, A., & Bharti, R. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-31964-9_17 · Cited by 22

Health information technology:Fallacies and Sober realities – Redux A homage to Bentzi Karsh and Robert Wears

Abbott, P. A., & Weinger, M. B. (2019) · Applied Ergonomics · doi:10.1016/j.apergo.2019.102973 · Cited by 22

Unsupervised deep learning-based medical image registration: a survey

Duan, T., Chen, W., Ruan, M., Zhang, X., Shen, S., & Gu, W. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad9e69 · Cited by 23

From failure to fusion: A survey on learning from bad machine learning models

Naser, M. Z. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103122 · Cited by 24

Cyber security threats in the microbial genomics era: implications for public health

Fayans, I., Motro, Y., Rokach, L., Oren, Y., & Moran‐Gilad, J. (2020) · Eurosurveillance · doi:10.2807/1560-7917.es.2020.25.6.1900574 · Cited by 21

Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence

Aslam, T. M., & Hoyle, D. C. (2021) · Ophthalmology and Therapy · doi:10.1007/s40123-021-00430-6 · Cited by 20

Detection of Adversarial Attacks against the Hybrid Convolutional Long Short-Term Memory Deep Learning Technique for Healthcare Monitoring Applications

Albattah, A., & Rassam, M. A. (2023) · Applied Sciences · doi:10.3390/app13116807 · Cited by 21

Model-Protected Multi-Task Learning

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