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

(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

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

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

Anonymized GCN: A Novel Robust Graph Embedding Method via Hiding Node Position in Noise.Preprint

Liu, A. (2020) · arXiv (Cornell University) · Cited by 4

Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial DataPreprint

Sadeghi, A., Wang, G., Ma, M., & Giannakis, G. B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03724 · Cited by 4

Malicious Adversarial Attacks on Medical Image Analysis

Thomas C. Winter (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.23250 · Cited by 4

Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray ImagesPreprint

Bortsova, G., Dubost, F., Hogeweg, L., Katramados, I., & de Bruijne, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00139 · Cited by 4

Medical AI and tort liability

Cohen, I. G., Slottje, A., & Gerke, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95068-8.00007-8 · Cited by 3

Decorrelative network architecture for robust electrocardiogram classification

Wiedeman, C., & Wang, G. (2024) · Patterns · doi:10.1016/j.patter.2024.101116 · Cited by 3

On factors that influence deep learning-based dose prediction of head and neck tumors

Gao, R., Mody, P., Rao, C., Dankers, F. J. W. M., & Staring, M. (2025) · Physics in Medicine and Biology · doi:10.1088/1361-6560/adcfeb · Cited by 3

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks Against LLMs

Bahar, A. A. M., & Wazan, A. S. (2025) · IEEE Access · doi:10.1109/access.2025.3574108 · Cited by 6

Machine Learning and AI in Microscopy Image Analysis

Gagniuc, E. (2025) · Springer eBooks · doi:10.1007/978-3-031-86270-0_7 · Cited by 3

Vulnerability to One-Pixel Attacksof Neural Network Architecturesin Medical Image Classification

Tajak, W., Nurzyǹska, K., & Piórkowski, A. (2025) · Bio-Algorithms and Med-Systems · doi:10.5604/01.3001.0055.3261 · Cited by 3

GuidedDE: Targeted black-box adversarial attacks via confidence-guided mutation on automatic speech recognition systems

Li, J., Song, L., & Feng, Y. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108338 · Cited by 3

Interpretable structural modeling of MR images using q − Bézier curves: A geometry-aware paradigm beyond deep learning

Özger, F., Onan, A., Turhan, N., & Özger, Z. Ö. (2026) · Information Sciences · doi:10.1016/j.ins.2026.123147 · Cited by 3

Medicine in the Age of Artificial Intelligence: Cybersecurity, Hybrid Threats and Resilience

Akrap, G., Dumić-Čule, I., Parlov, N., & Jandroković, S. (2026) · Applied Sciences · doi:10.3390/app16041736 · Cited by 5

Co-Lifecycle Governance for Learning Medical AI: A Hybrid Convergence Framework for Adaptive Regulatory Oversight

Lee, J. H., Choi, B., Jeong, K., Suh, S. W., Kim, J. H., & Son, D.-S. (2026) · Journal of Medical Internet Research · doi:10.2196/90654 · Cited by 3

Digital twins of ex vivo human lungs enable accurate and personalized evaluation of therapeutic efficacy

Zhou, X., Wang, B., Wei, Y., Hacker, S., Kim, S., Borrillo, T., McCaig, A., Ahmed, H., Ren, Y., Hough, O., et al. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03121-4 · Cited by 3

Operationalizing WHO Ethical Principles for Healthcare AI: A Lifecycle-Aligned Governance-by-Design Framework

Saraboji, K., Gopalakrishnan, K., Sood, D., Kaur, A., Shivaram, S., Helgeson, S. A., Arunachalam, S. P., & Mitra, D. (2026) · AI in Medicine · doi:10.3390/aimed1020016 · Cited by 4

Anomaly Recognition Algorithm for Human Multipose Motion Behavior Using Generative Adversarial Network

Zhang, N., Ren, J., Xu, Q., Wu, H., & Wang, M. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/2656001 · Cited by 3

Adversarial anchor-guided feature refinement for adversarial defense

Lee, H., & Ro, Y. M. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104722 · Cited by 3

Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI

Liu, X., Rivera, S. C., Moher, D., Calvert, M., Denniston, A. K., & y CONSORT-AI, G. D. T. S.-A. (2024) · Revista Panamericana de Salud Pública · doi:10.26633/rpsp.2024.13 · Cited by 5

Exploring Structural Sparsity of Coil Images from 3-Dimensional Directional Tight Framelets for SENSE Reconstruction

Li, Y., Chan, R. H., Shen, L., Zhuang, X., Wu, R., Huang, Y., & Liu, J. (2024) · SIAM Journal on Imaging Sciences · doi:10.1137/23m1571150 · Cited by 3

AdaSVaT: Adaptive Singular Value Thresholding for Adversarial Detection in Fundus Images

Joseph, N., George, S. N., Ameer, P. M., & Raja, K. B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78398-2_25 · Cited by 3

Analyzing the Impact of Adversarial Examples on Explainable Machine Learning

Devabhakthini, P., Nayak, S. C., Shukla, R., Parida, S., & Das, T. (2025) · Cyber Awareness and Research Symposium (CARS) · doi:10.1109/cars67163.2025.11337828 · Cited by 6

Two-Way Feature-Aligned And Attention-Rectified Adversarial Training

Zhang, H., Jia, F., Zhang, Q., Han, Y., Kuang, X., & Tan, Y. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102777 · Cited by 3

On the Adversarial Robustness of Linear Regression

Li, F., Lai, L., & Cui, S. R. (2020) · Machine learning for signal processing ... · doi:10.1109/mlsp49062.2020.9231839 · Cited by 3

Transdisciplinary AI Observatory—Retrospective Analyses and Future-Oriented Contradistinctions

Aliman, N.-M., Kester, L. J. H. M., & Yampolskiy, R. V. (2021) · Philosophies · doi:10.3390/philosophies6010006 · Cited by 21

AI for Humanity: The Global Challenges

Maclure, J., & Russell, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69128-8_8 · Cited by 3

Adversarial Attacks Medical Diagnosis Model with Generative Adversarial Networks

Wang, X., Lv, S., Sun, J., & Wang, S. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-89698-0_69 · Cited by 3

MODERN ADVANCED ARTIFICIAL INTELLIGENCE FOR SMART MEDICINE

Kolesnichenko, O. Y., Martynov, A. V., SP.ARM, S. P., Pulit, V. V., SP.ARM, S. P., Kolesnichenko, Y., Uzgraph.ru, M., Shakirov, V. V., Mazelis, L., Varlamov, O. O., et al. (2019) · Remedium Journal about the Russian market of medicines and medical equipment · doi:10.21518/1561-5936-2019-04-36-43 · Cited by 3

Optimized Parameter Search Approach for Weight Modification Attack Targeting Deep Learning Models

Echeberria-Barrio, X., Gil, A., Orduna-Urrutia, R., & Mendialdua, I. (2022) · Applied Sciences · doi:10.3390/app12083725 · Cited by 3

PHASE: Security Analyzer for Next-Generation Smart Personalized Smart Healthcare System

Haque, N. I., & Rahman, M. A. (2022) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh55609.2022.00040 · Cited by 3

Towards Adversarial Robustness with Early Exit Ensembles

Qendro, L., & Cecilia Mascolo, C. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871347 · Cited by 3

ECG-ATK-GAN: Robustness Against Adversarial Attacks on ECGs Using Conditional Generative Adversarial Networks

Hossain, K. F., Kamran, S. A., Tavakkoli, A., & Ma, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17721-7_8 · Cited by 5

Vloga umetne inteligence v izobraževanju in za izobraževanje

Pesek, I., & Krašna, M. (2022) · doi:10.18690/um.ff.11.2022.13 · Cited by 3

Potential Pitfalls of Remote and Automated Video Assessments of Movements Disorders

Park, K. W., Wu, H. J., Yu, T., Mahal, R., Mirian, M. S., & McKeown, M. J. (2023) · Movement Disorders · doi:10.1002/mds.29325 · Cited by 3

Prescribing medications with indications: time to flip the script

Schiff, G. D., Lambert, B. L., & Wright, A. T. (2023) · BMJ Quality & Safety · doi:10.1136/bmjqs-2023-015923 · Cited by 3

TENET: a new hybrid network architecture for adversarial defense

Tuna, Ö. F., Çatak, F. Ö., & Eskil, M. T. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00675-1 · Cited by 3

Distributionally robust learning-to-rank under the Wasserstein metric

Sotudian, S., Chen, R., & Paschalidis, I. C. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283574 · Cited by 3

Enhanced Defensive Model Using CNN against Adversarial Attacks for Medical Education through Human Computer Interaction

Priya, K. V., & Peter, J. D. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2204697 · Cited by 3

Generalisation and reliability of deep learning for digital pathology in a clinical setting

Pocevičiūtė, M. (2023) · Linköping studies in science and technology. Dissertations · doi:10.3384/9789180753005 · Cited by 4

A Multimodal Adversarial Database: Towards A Comprehensive Assessment of Adversarial Attacks and Defenses on Medical Images

Hu, J., He, Y., Zhang, W., Pang, S., Ma, R., & Du, A. (2023) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa60987.2023.10302594 · Cited by 3

On the Interplay of Convolutional Padding and Adversarial Robustness

Gavrikov, P., & Keuper, J. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00430 · Cited by 3

D-BADGE: Decision-Based Adversarial Batch Attack With Directional Gradient Estimation

Yu, G., Jeon, M., & Hwang, H. (2024) · IEEE Access · doi:10.1109/access.2024.3407097 · Cited by 4

Quantum inception score

Sone, A., Tanji, A., & Yamamoto, N. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.033198 · Cited by 3

Review of Intrusion Detection Systems for Supervisor Control and Data Acquisition: A Machine Learning Approach

Alberto, H. D. C., Dembélé, J. M., Diop, I., & Bah, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-72287-5_3 · Cited by 3

Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans

Fok, W. Y. R., Fieselmann, A., Huemmer, C., Biniazan, R., Beister, M., Geiger, B., Kappler, S. G., & Saalfeld, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-73363-2 · Cited by 3

Semantic Web-Driven Targeted Adversarial Attack on Black Box Automatic Speech Recognition Systems

Li, J., Feng, Y., & Wang, M. (2024) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.360651 · Cited by 3

Fortifying Trust: Can Computational Reliabilism Overcome Adversarial Attacks?

Pawłowski, P., & Barman, K. G. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00851-2 · Cited by 3

Comparison of the impact of rectal susceptibility artifacts in prostate magnetic resonance imaging on subjective evaluation and deep learning: a two-center retrospective study

Wang, Z., Lu, P., Liu, S., Fu, C., Ye, Y., Yu, C., & Hu, L. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01602-7 · Cited by 3

Deep learning advances in breast medical imaging with a focus on clinical readiness and radiologists’ perspective

Oladimeji, O. O., Imran, A.-A.-Z., Wang, X., & Unnikrishnan, S. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105601 · Cited by 5

Framing the effects of machine learning on science

Neto, V. J. D. S., Bonacelli, M. B. M., & Pacheco, C. A. (2022) · AI & Society · doi:10.1007/s00146-022-01515-x · Cited by 4

The Dynamic Pathosome: A Surrogate for Health and Disease

Lenárt, P., Scheringer, M., & Vašků, J. B. (2020) · Healthy ageing and longevity · doi:10.1007/978-3-030-52663-4_16 · Cited by 3

Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical imagesPreprint

Ghosal, S., & Shah, P. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.05791 · Cited by 3

Self-supervised learning for early detection of neurodegenerative diseases with small data

Jiang, H. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/166402 · Cited by 3

Artificial intelligence and cybercrime: implications for individuals and the healthcare sector

Monteith, S., Glenn, T., Geddes, J. R., Achtyes, E. D., Whybrow, P. C., & Bauer, M. (2024) · The British Journal of Psychiatry · doi:10.1192/bjp.2024.77 · Cited by 6

Machine Learning in Medical Systems

Hu, W. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197653609.013.28 · Cited by 3

Development and validation of machine learning predictive models for assessing dialysis adequacy in dialysis patients

Zhou, J., Wu, H., Zhang, L., Zhang, Q., Wang, J., Zhao, H., Dang, Y., Zhang, S., & Li, L. (2025) · The International Journal of Artificial Organs · doi:10.1177/03913988251355082 · Cited by 2

Future‐Proofing AI Models in IoMT Environment: Adversarial Dataset Generation and Defense Strategies

Alsubai, S., Karovič, V., Almadhor, A. S., Hejaili, A. A., Juanatas, R. A., & Sampedro, G. A. (2025) · Digital twins and applications. · doi:10.1049/dgt2.70010 · Cited by 2

ClusFed: A Clustering-Based Defense for Secure Federated Learning

Kabbaj, H., El-Azouzi, R., & Kobbane, A. (2025) · IEEE Access · doi:10.1109/access.2025.3605870 · Cited by 2

AI in medicine: An analysis of threats, risks, and solutions for the future of healthcare

Brawańska, H., Jędrzejowska, M., Lau, K., & Kasperczyk, J. (2025) · Annales Academiae Medicae Silesiensis · doi:10.18794/aams/207480 · Cited by 3

Adversarial Machine Learning in Healthcare: Risks to AI-Driven Diagnostics and Treatment Plans

Soberano, K. T., & Condes, K. A. (2025) · International Journal of Statistics in Medical Research · doi:10.6000/1929-6029.2025.14.71 · Cited by 2

Ethical and equitable approaches in AI for vector-borne disease management

Williams, J. J., Angelidou, I. A., Cholvi-Simó, M., Kadriaj, P., Martinou, A. F., Nadejda, M., Ong, S., Sadak, F., Skuhrovec, J., Velo, E., et al. (2025) · AI and Ethics · doi:10.1007/s43681-025-00933-z · Cited by 4

Advances, Evaluation, and Explainability of Large Language Models in Healthcare: A Systematic Review

Amin, S. U., Guizani, M., & Hossain, M. S. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3786334 · Cited by 5

Secondary use of health data: applications, models, algorithms, and ethical considerations

Soliman, M., Abdelziz, O., Radwan, A. A., Shehata, M. S., Ronquillo, C., & Shearer, K. (2026) · AI and Ethics · doi:10.1007/s43681-026-01017-2 · Cited by 4

Robustness enhancement of grey prediction models in energy consumption based on adversarial training

Luo, X., Duan, H., Tian, Y., & Jin, X. I. A. O. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114902 · Cited by 2

Artificial intelligence in the prehospital setting – potentials, challenges, and practice-relevant fields of application in emergency medical services

Machado, C., & Hessel, S. (2026) · BMC Artificial Intelligence · doi:10.1186/s44398-026-00027-8 · Cited by 3

Defending Against Model Inversion Attacks for Biomedical Images via Learnable Data Perturbation

Jiang, S., Firouzi, F., & Chakrabarty, K. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3693688 · Cited by 3

Agentic systems in computational pathology: architectures, evidence, and translational challenges

Lu, X., Li, Q., Gao, Y., Wei, D., Lyu, M., Ma, S., Li, J., Wu, Y., Cai, L., Zhang, T., et al. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-08829-0 · Cited by 2

A Review on Parkinson's Disease Detection Methods: Traditional Machine Learning Models vs. Deep Learning Models

Ahmed, M. T., Mondal, M. N. I., Gupta, D., & Ali, M. S. (2022) · European Journal of Information Technologies and Computer Science · doi:10.24018/compute.2022.2.3.67 · Cited by 2

Towards a Visualizable, De-identified Synthetic Biomarker of Human Movement Disorders

Hu, H., Xiao, D., Rhodin, H., & Murphy, T. H. (2022) · Journal of Parkinson s Disease · doi:10.3233/jpd-223351 · Cited by 2

Log-related Coding Patterns to Conduct Postmortems of Attacks in Supervised Learning-based Projects

Bhuiyan, F. A., & Rahman, A. (2022) · ACM Transactions on Privacy and Security · doi:10.1145/3568020 · Cited by 2

Achievements and prospects for the application of artificial intelligence technologies in medicine. Overview. Part 2

Бердутин, В. А., Abaeva, O. P., Romanova, T. E., & Romanov, S. V. (2023) · Sociology of Medicine · doi:10.17816/socm107908 · Cited by 2

Robustness for ECG Classification by Adversarial Training Over Clinical Features

Jain, S., Sangroya, A., Vig, L., & Anantaram, C. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-36938-4_18 · Cited by 2

Place de l’intelligence artificielle dans la chirurgie de l’épaule en 2023

Abadie, P., Pirlot, P., & Virieux, B. (2023) · Revue de Chirurgie Orthopédique et Traumatologique · doi:10.1016/j.rcot.2023.07.008 · Cited by 2

Effects on Selective Removal of Adversarial Examples for Noisy X-ray Images

Namura, H., Itasaka, T., & Okuda, M. (2023) · International Conference on Intelligent Medicine and Image Processing (IMIP) · doi:10.1109/imip57114.2023.00019 · Cited by 2

Using a deep generation network reveals neuroanatomical specificity in hemispheres

Wang, G., Jiang, N., Ma, Y., Suo, D., Liu, T., Funahashi, S., & Yan, T. (2024) · Patterns · doi:10.1016/j.patter.2024.100930 · Cited by 1

Robust Adversarial Defense by Tensor Factorization

Bhattarai, M., Kaymak, M. C., Barron, R., Nebgen, B. T., Rasmussen, K. Ø., & Alexandrov, B. S. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00050 · Cited by 2

Beyond Recognition : Privacy Protections in a Surveilled World

Le, M. (2024) · Linköping studies in science and technology. Dissertations · doi:10.3384/9789180756761 · Cited by 2

Model-data-driven adversarial active learning for brain tumor segmentation

Ma, S., Mathur, P., Ju, Z., Lawlor, A., & Dong, R. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108585 · Cited by 2

Safeguarding Medical AI: Insights and Addressing Adversarial Threats in Consumer Electronics

Jha, B. K., Khowaja, S. A., Dev, K., & Pandey, A. (2024) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2024.3443543 · Cited by 2

Prediction and Analysis of Digital Health Records, Geonomics, and Radiology Using Machine Learning

Raj, S., Agarwal, A. P., Tripathi, S., & Gupta, N. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815305128124010005 · Cited by 2

A Comprehensive Survey on Federated Learning and its Applications in Health Care

Pacheco, S. A. B. (2024) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet62352.2024.10730687 · Cited by 2

Attacking medical images with minimal noise: exploiting vulnerabilities in medical deep-learning systems

Wang, W., Wildgruber, M., & Wang, Y. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-24-1764 · Cited by 4

Detecting Adversarial Samples using Kernel Density Feature Extractor in Medical Image

Das, S., Keserwani, P. K., & Govil, M. C. (2025) · International Conference on Distributed Computing and Networking (ICDCN) · doi:10.1145/3700838.3703675 · Cited by 2

Investigating the Impact of Adversarial Attacks on Deep Learning-Based Wearable Robot Controllers: Security, Reliability, and Safety Concerns

Swami, C. P., & Joshi, D. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3563520 · Cited by 2

Neural predictor aided policy optimization for adversarial controlled sensing

Kalouptsidis, N., & Stamatelis, G. (2025) · Signal Processing · doi:10.1016/j.sigpro.2025.110115 · Cited by 2

Anwendungen, Herausforderungen und ein vertrauenswürdiger Umgang mit künstlicher Intelligenz im Bereich Public Health

Grah, J. S., Irrgang, C., Schaade, L., Ladewig, K., & Körber, N. (2025) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-025-04098-2 · Cited by 2

CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models

Rafferty, A., Ramaesh, R., & Rajan, A. (2025) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi64645.2025.00057 · Cited by 5

Adversarial attacks on hybrid classical-quantum deep learning models for histopathological cancer detection

Baral, B., Bhalgamiya, B., Majumder, R., Roy, D. D., & Roy, T. D. (2025) · APL Machine Learning · doi:10.1063/5.0270673 · Cited by 2

Distilling Adversarial Robustness Using Heterogeneous Teachers

Deng, J., Palmer, A., Mahmood, R., Rathbun, E., Bi, J., Mahmood, K., & Aguiar, D. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.07.114 · Cited by 2

Instability in deep learning – when algorithms cannot compute uncertainty quantifications for neural networks

Gazdag, L. E., Antun, V., & Hansen, A. C. (2025) · European Journal of Applied Mathematics · doi:10.1017/s095679252510017x · Cited by 2

The need for a systematic machine-learning process: A proposal via a mobile malware classification case study

Canbek, G. (2021) · doi:10.1109/iscturkey53027.2021.9654378 · Cited by 2

Robustness Assurance Quotient: Demonstrating Context Matters for AI Performance and ML Security

Lefcourt, S., Gordon, N. G., Wong, H., & Falco, G. J. (2022) · IEEE International Conference on Assured Autonomy (ICAA) · doi:10.1109/icaa52185.2022.00012 · Cited by 2

A Simple Strategy to Provable Invariance via Orbit Mapping

Gandikota, K. V., Geiping, J., Lähner, Z., Czapliński, A., & Möller, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26348-4_23 · Cited by 2

Scientistic reductionism and the “dark side” of modern medicine: A personal reflection

Shelley, B. P. (2019) · Archives of Medicine and Health Sciences · doi:10.4103/amhs.amhs_163_19 · Cited by 2

Autonomous Artificial Intelligence Safety and Trust

Abràmoff, M. D. (2021) · Springer eBooks · doi:10.1007/978-3-030-78601-4_4 · Cited by 4

AGS: Attribution Guided Sharpening as a Defense Against Adversarial Attacks

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PhD Forum: Learning Adaptive Adversarial Attacks for Medical AI via Reinforcement Learning

Fabiano, M. (2026) · doi:10.1109/smartcomp-companion70724.2026.00091

Reply to “Malicious Adversarial Attacks on Medical Image Analysis”

Desjardins, B., & Ritenour, E. R. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.24244

Adversarial Threats to AI Systems: A Critical Review of Vulnerabilities in Machine Learning Security

Nataala, A. A. (2026) · doi:10.13140/rg.2.2.12207.50081

Dependence of the results of adversarial attacks on medical image modality, attack type, and defense methods

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Malicious Adversarial Attacks on Medical Image Analysis

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