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

Cross-Input Certified Training for Universal Perturbations

Xu, C., & Singh, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73650-6_14

Adversarial Attacks and Defenses in Deep Learning for Healthcare ApplicationsPreprint

Nagarjuna, T., Srividya, G., Durgam, R., Nagasri, A., Nabi, S. A., & Rajender, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5085205

CHBaR: Conditional Hilbert Schmidt Bottleneck as Regularization for Adversarial Robustness

Jung, S., Hong, J.-H., Kim, H.-D., & Lee, S. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831674

Cybersecurity Threats in Medical Laboratories: Time to Take Precautions

Bhatia, S. (2024) · Medical Science and Discovery · doi:10.36472/msd.v11i6.1166

Safe Breast Cancer Diagnosis Resilient to Mammographic Adversarial Samples

Hao, D., Arefan, D., Zuley, M. L., Berg, W. A., & Wu, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77789-9_23

Unveiling the veil: high-frequency components as the key to understanding medical DNNs’ vulnerability to adversarial examples

Qian, Y., Tao, R., Du, H., & Wang, B. (2025) · Cybersecurity · doi:10.1186/s42400-024-00330-9

Proactively Approaching Cybersecurity With AI-Powered Malware Detection Is Essential

Agrawal, S., Pandey, L., & Lakshmi, D. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9919-4.ch002

Artificial Intelligence in Healthcare: Opportunities, challenges, and secure implementation

Bompally, S. D. (2025) · Global Journal of Engineering and Technology Advances · doi:10.30574/gjeta.2025.23.1.0135

The Dark Side of AI

Mitra, U., & Rehman, S. U. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2200-1.ch001

Mathematical Foundations of Social Intelligence

Wang, D., Shang, L., & Zhang, Y. (2025) · Springer eBooks · doi:10.1007/978-3-031-90080-8_3 · Cited by 1

IRC-SC: Improving Robustness of CNN-Based MRI System Using SRGAN and CNN

Patel, G., Chaturvedi, V., & Kumar, S. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00095

Cybersecurity Should be a Routine Component of Healthcare Provider Education

Piotrowski, B., Seth, I., Kovoor, J. G., Sorich, M. J., & Bacchi, S. (2025) · ANZ Journal of Surgery · doi:10.1111/ans.70258

FedPGD: Federated Learning with Projected Gradient Descent for Catheter and Guidewire Segmentation

Kongtongvattana, C., Huang, B., Nguyen, H. A., Olajide, O., & Nguyen, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-92011-0_7

Floating-Point Neural Networks are Provably Robust Universal Approximators

Hwang, G., Lee, W., Park, Y., Park, S., & Saad, F. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98679-6_14 · Cited by 2

Evaluation of Adversarial Noise Attacks on Ai Systems Using Image Processing Techniques

Su, D. C. S., Sim, K. S., & Abas, F. S. (2025) · IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) · doi:10.1109/iscaie64985.2025.11081093

Autonomous Artificial Intelligence Safety and Trust

Abràmoff, M. D. (2025) · Springer eBooks · doi:10.1007/978-3-031-83756-2_4

The Evolution of Adversarial Attacks in Deep Learning: A Holistic Survey

Singh, S., Katiyar, A., & Singh, A. S. (2025) · International Conference on Networks and Cryptology (NETCRYPT) · doi:10.1109/netcrypt65877.2025.11102147

Hierarchical Adversarial Policy Learning for Robust and Explainable Defence in Ophthalmic Artificial Intelligence Systems

Alrehaili, D. A., Wishi, D. H., Mugharbel, L. H., Alhazmi, M. A., Azzoni, R. I., Alhazmi, S. M., Alsenani, T. A., & Morfeq, H. A. (2025) · Journal of Advanced Trends in Medical Research · doi:10.4103/atmr.atmr_55_25

Certified Local Transferability for Evaluating Adversarial Attacks

Chen, M., Li, J., Wu, L.-I., & Li, G. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3602931

Exploring Secure Machine Learning Through Payload Injection and FGSM Attacks on ResNet-50

Yadav, U., Niroula, S., Gupta, G. K., & Yadav, B. (2025) · Silicon Valley Cybersecurity Conference (SVCC) · doi:10.1109/svcc65277.2025.11133652

Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning

Mahmood, K., Rathbun, E., Sahu, R., van Dijk, M., Ahmad, S., & Ding, C. (2025) · IEEE Access · doi:10.1109/access.2025.3608117

Meta-Distillation: A Novel Adversarial Distillation Network for Adversarial Robustness

Okumali, R., & Li, J. (2025) · IEEE International Conference on Pattern Recognition and Machine Learning (PRML) · doi:10.1109/prml66062.2025.11159673

AI Driven Medical Diagnostics with Improved Robustness to Adversarial Perturbations

Yinusa, A., & Faezipour, M. (2025) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa65407.2025.11166241

AI-Driven Estimation of Chronological Age Using Photoplethysmography

Cho, N., Lee, S.-H., & Lee, S.-B. (2025) · Official Journal of Asia Pacific Anti-Aging Society · doi:10.70966/anti-aging.25.0007

Longitudinal Mammogram Exam-Based Breast Cancer Diagnosis Models: Vulnerability to Adversarial Attacks

Zhou, Z., Hao, D., Arefan, D., Zuley, M. L., Sumkin, J. H., Grimm, L. J., Joshi, J., & Wu, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05559-0_35

Birds look like cars: adversarial analysis of intrinsically interpretable deep learning

Baniecki, H., & Biecek, P. (2025) · Machine Learning · doi:10.1007/s10994-025-06896-w

Ensuring Robustness of Large Language Models: Cross-Sectoral Challenges and Quality Solutions to Adversarial Attacks

Nebrisi, E. E., & Khanday, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9248-4_39

Securing U.S. Healthcare Infrastructure with Machine Learning: Protecting Patient Data as a National Security Priority

Hasan, N., Rasel, I. H., Rahman, M. A., Islam, M. K., Arman, M., & Jahan, N. (2022) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.3987

Artificial Intelligence in Healthcare

Ahmad, F. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5727-0.ch002

A survey on clinical practice assisted by artificial intelligence in cardiovascular medicine

Liang, C., Zhang, Y., & Shi, J. (2025) · Asian Heart Journal · doi:10.1097/ah9.0000000000000019

Lung Disease Detection Using Deep Learning

Meenatchi, M. I. M., R, P. B., & Srivarun, S. (2025) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-29264

Adversarial Attacks in Medical Deep Learning System

Lau, Y. K., Loh, B. C. S., Vong, W.-T., & Then, P. (2025) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-96518-0_4

Beyond Pattern Recognition: Teaching AI to Think Critically Before It Learns

Xie, X., Zhang, J., & Voas, J. (2025) · Computer · doi:10.1109/mc.2025.3599970 · Cited by 2

Adversarially Robust Hybrid Long Short-term Memory Architecture with Integrated Missing Data Handling for Bladder Diary Analytics

Farghal, D. M., Alshanqiti, A. I., Al-Ahmadi, A. O., Taha, A. J., Badawi, A. S., Alahmadi, R. N., Fallatah, A. A., Alharbi, A. A., & Albinsaad, L. S. (2025) · Journal of Advanced Trends in Medical Research · doi:10.4103/atmr.atmr_95_25

Cybersecurity Challenges in Multimodal Medical Data: A Critical Review with a Focus on Diabetic Retinopathy Screening Systems

Esserkassi, B., Eddarouich, S., & Bourouhou, A. (2025) · Data & Metadata · doi:10.56294/dm20251248

Universal black-box attacks against a third-party Alzheimer’s diagnostic system

Sigvard, C. S., Franco-Valiente, J. M., & Mato, G. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae1baf

Do stable neural networks exist for classification problems? – A new view on stability in AI

Liu, D., & Hansen, A. C. (2025) · European Journal of Applied Mathematics · doi:10.1017/s0956792525100181

A Comparative Analysis of Adversarial Attacks using Machine Learning Techniques

Sharma, B. K., Ratna, S., & Kumar, R. (2025) · doi:10.1109/cises66934.2025.11265035

Precision Healthcare: Objectives, Challenges, and Solutions

Kakulapati, V., Srinivas, M. S., Dasika, S. R. C. M., & Paramashivam, A. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898813512125010004

Additional file 1 of Universal adversarial attacks on deep neural networks for medical image classification

Hirano, H., Minagi, A., & Takemoto, K. (2021) · Figshare · doi:10.6084/m9.figshare.13543897

The Future of Deep Learning in Computational Mechanics

Herrmann, L., Jokeit, M., Weeger, O., & Kollmannsberger, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-89529-6_11

Knowledge, Readiness, and Perception of Medical Students Toward Medical Artificial Intelligence: A Cross-Sectional Study

Borzu, Z. A., Keikha, L., & Shahraki-Mohammadi, A. (2025) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205251407758 · Cited by 1

Towards Better Adversarial Defense on Lung CT Nodule Classification Models with Feature Enhancement

Zhu, Y., Tian, Y., & Chien, A. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-13800-2_4

The Security of Deep Learning Defenses in Medical Imaging

Levy, M., Amit, G., & Mirsky, Y. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-13800-2_5

Ethical Considerations in AI-Driven Drug Development and Pharmaceutical Industry

Bari, Y. C., Wani, M. S., Nagare, G. D., Chogale, M. M., & Pai, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5258-9.ch010

Blockchain-Enhanced Clinical Risk Management for AI Healthcare Systems : Privacy-Preserving Incident Response and Compliance Monitoring

Makinde, O. F., Duruemeruo, U. C., Eziokwu, U. J., Olasehan, O. A., Ani, S. F., & Adebayo, A. (2021) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset214419

Clinical Cyberbioethics and AI-Mediated Clinical Decision-Making: a mapping and narrative review with thematic synthesis (SWiM)Preprint

Pérez, A. D., & Pérez, W. A. (2026) · Research Square · doi:10.21203/rs.3.rs-8585342/v1

The Impact of Artificial Intelligence in Dentistry and Oral Medicine: Ethical Issues and Challenges from an Oral Clinical’s Perspective

Harte, M., Feng, Q. J., Carey, B., Addison, O., Tan, Z. Q., Li, Y., & Albuquerque, R. (2026) · Springer eBooks · doi:10.1007/978-981-96-7448-0_22-1

Adversarial Attack Resilient ML-Assisted Golden Free Approach for Hardware Trojan Detection

Ghimire, A., Alkurdi, M., Ghajari, G., Hossain, M. A., & Amsaad, F. (2026) · Microelectronics · doi:10.3390/microelectronics2010002

A Multi-Layer Security Framework for Adversarial SQL Injection in Machine Learning Systems

Veigas, C. M., Mrs. Sangeetha, G., & Kulkarni, M. (2025) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn67655.2025.11368126

Edge AI Empowered Predictive Analytics for Smart Healthcare

Mulo, J., Liang, H., Deng, L., Xu, G., & Yu, W. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-06658-9_5

Pulling Back the Curtain: Unsupervised Adversarial Detection via Contrastive Auxiliary Networks

Mizrahi, E., Lapid, R., & Sipper, M. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00242

Impact of Black-Box Adversarial Attacks on Deep Neural Networks for Skin Imaging

Moniak, B., & Jaworek-Korjakowska, J. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00123

Brain Tumor Classification Based on Machine Learning and Deep Learning Technologies: A Comprehensive Review

Aamir, M., Rahman, Z., Bhatti, U. A., Bhutto, J. A., Choudhry, N., & Abro, W. A. (2025) · International Conference on Computer, Vision and Intelligent Technology (ICCVIT) · doi:10.1109/iccvit67848.2025.11391405

Histopathology Image Classification: Performance, Efficiency, and Adversarial Robustness

Shahpar, M. A., & Akbari-Shahpar, M. (2025) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme68496.2025.11392428

GAI & cybersecurity: Bi-visions

Taher, A. B., Fourati, L. C., & Ayed, S. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.100940

Security and risk governance in AI-driven medical imaging systems: An uncertainty-aware framework

Ahmad, S., Kumar, K., Prakash, A., & Shahnawaz (2026) · EDPACS · doi:10.1080/07366981.2026.2640131

Attention Reversal Driven Cross-Model Black-Box Attack on RFFI

Li, M., Chai, Z., Kurbonov, S., Wang, H., Chen, W., & Yang, X. (2026) · IEEE Access · doi:10.1109/access.2026.3670940

Artificial Intelligence in Dentistry and Oral Medicine: Ethical Issues and Challenges

Harte, M., Feng, Q. J., Carey, B., Addison, O., Tan, Z. Q., Li, Y., & Albuquerque, R. (2026) · Springer eBooks · doi:10.1007/978-981-96-7448-0_22-2

ISIC Under Attack! Robustness of Skin Lesion Classifiers Under Adversarial Perturbations

Moniak, B., Brodzicki, A., & Jaworek-Korjakowska, J. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00189

Ethical Use of Artificial Intelligence and Addressing Algorithmic Bias in Healthcare Cybersecurity

Abdurrazaq, A. O., Oladejo, A. I., & Camara, O. D. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-6696-8.ch006

Digital Devices in Psychiatry and Mental Health

Étienne, H. I. E. N. (2026) · doi:10.1002/9781394446025.ch3

Mixed Perturbation: Generating Directionally Diverse Perturbations for Adversarial Training

Hyun, C., & Park, H. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01225-1

Enhancing robustness against adversarial attacks in biomedical image classification through squeezed quantized self-supervised contrastive learning

Punn, N. S., & Tapaswi, S. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110093

Lightweight and interpretable integrated diagnostic framework for skin lesion segmentation and classification

Fan, W., Zhang, Y., Fu, K., Shen, Z., Li, X., & Li, X. (2026) · Information Sciences · doi:10.1016/j.ins.2026.123429

SEMPRA: Secure medical privacy-reversible adversarial framework

Xie, X., Ouyang, C., Du, X., Zhou, J., Pun, C., Chang, C.-C., & Chang, C.-C. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115142

TRADES-Based Defense Against Adversarial Attacks in Medical Image Classification

Jasso, K., Tollett, A., Tuba, I., & Tuba, E. (2026) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs69419.2026.11459020

Données de santé, dynamiques et enjeux de souveraineté

Pinilla, E. (2023) · theses.fr (ABES) · doi:10.70675/267dba09z6c05z4a5dz830ezd278df3e3fa1

Deep learning methods for localization, segmentation and robustness in medical imaging

Laousy, O. (2024) · theses.fr (ABES) · doi:10.70675/4ffc4285z667dz430czb4d9za731fb5f4d27

The Pillars of Trust in Life Science AI

Wang, Z., Sannyasi, A., & Jiang, J. (2026) · Springer eBooks · doi:10.1007/978-3-032-14222-1_8

A review of the comparative study of anomaly detection techniques for enhancing security in AI-driven medical applications

Thakur, S., Bhatele, M., & Waoo, A. A. (2026) · Next research. · doi:10.1016/j.nexres.2026.101744

Hybrid adversarial distilled defense for deep learning-based breast cancer detection

Das, S., Noor, F., Raim, T. Y., Das, B., Bhowmik, A., Nandi, D., Rahman, M. S., & Karmaker, D. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae60c9

A Systematic Evaluation of Data Poisoning Severity Under Label Flipping and Gradient Manipulation Based Attacks

Perry, S., Jiang, Y., & Walter, C. (2026) · IEEE SoutheastCon · doi:10.1109/southeastcon63549.2026.11476200

MedAssist: Towards Explainable Multi-Task ICU Risk Prediction for Clinical Deployment

Norwood, T., Claiborne, J., Chatterjee, P., Das, D., & Ghosh, U. (2026) · IEEE SoutheastCon · doi:10.1109/southeastcon63549.2026.11476489

Adaptive Defense Against Stationary Test-Time Attacks on Classifiers

Yan, A., Kim, J., & Raich, R. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464866

Operational and Translational Challenges of AI in Ophthalmology Imaging Systems

Espaillat, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-19336-0_23

Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment

Basu, S. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00267

Symmetry-Driven Multimodal Adversarial Attacks: An Information-Theoretic Perspective on Cross-Modal Invariance and Robustness

Wei, J., Wang, X., Xu, L., & Li, Y. (2026) · Entropy · doi:10.3390/e28050521

Cybersecurity is imperative in robotic arthroplasty

Bagaria, V. B., Vaishya, R., Vaish, A., & Lustıg, S. (2026) · SICOT-J · doi:10.1051/sicotj/2026019

A Multi-layered Security Framework for Trustworthy Expert Systems: Experimental Validation in Healthcare, Business, and Education

Chopra, A. B., Singh, M. K., Rani, K. S., Devi, S., & Kumari, M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-20114-0_39

The Double-Edged Sword: AI in Healthcare and the Rising Cybersecurity Threats

Ali, R. F., Ali, R. N., Irfan, W., & Hassan, B. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-20121-8_11

A Resilient Deep Learning Based Model for Automated Detection of Cardiovascular Abnormalities in ECG Images with Grad-CAM XAI

Sarkar, M. S., Rahman, K. M., Noor, N. U., Hossain, M. A., Karim, R., & Rifat, H. H. (2025) · IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON) · doi:10.1109/becithcon69222.2025.11504179

Current Limitations and Considerations

Dawa, H., Grixti, T. M., & Cortes, A. R. G. (2026) · Springer eBooks · doi:10.1007/978-3-032-24895-4_5

Safe-By-Design Clinical LLMs: Formal Risk Models and Silent-Trial Protocols for Workflow Integration

Gayapu, C. T. (2025) · Macaw International Journal of Advanced Research in Computer Science and Engineering · doi:10.70162/mijarcse/2025/v11/i2/v11i202

Ethical and Legal Challenges in AI-Based Diagnosis and Treatment

Mishra, S., & Gupta, P. (2026) · Cognitive systems monographs · doi:10.1007/978-3-032-18782-6_9

Ethical Implementations of Artificial Intelligence in Craniofacial and Facial Plastic Surgery

Allam, O., Kong, V., & Alperovich, M. A. (2026) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000012862

Adversarial Robust Federated Learning for Secure Mortality Risk Prediction using Multi-Institutional Electronic Health Records (EHRs)

Chatterjee, S., & Satpathy, S. (2026) · Journal of Visualized Experiments · doi:10.3791/69104

Fluoroscopy-Guided Motion Management in Particle Therapy: Evolution, Challenges, and AI-Enabled Opportunities

Li, F., Furutani, K. M., & Beltran, C. J. (2026) · Tomography · doi:10.3390/tomography12050066

Are Existing AI Laws Sufficient to Address Emerging Threats in Healthcare AI? (Preprint)Preprint

Singh, H., Sr., Rana, S., Bhadada, S., Duggal, P., Goel, P., Bhagat, P., & Balsari, S. (2026) · doi:10.2196/preprints.100411

TrustHeart: Explainable and Privacy-Preserving AI Architecture for Trustworthy Cardiac Monitoring in IoMT Consumer Applications

Liu, Q., Tang, J., Shen, J., Cai, Y. Y., Huang, Y. L., & Lu, N. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3695954

Adversarial Robustness of CNN, Transformer, and Hybrid Architectures in Medical Imaging AI

Altarawneh, E., & Alshdaifat, E. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-20544-5_9

Adversarial vulnerability of machine-learning surrogates for computational fluid dynamics

Chen, J., Cao, X.-Y., & Zhang, J.-J. (2026) · Physical Review Research · doi:10.1103/n5kp-jjcp

Smart diagnostics: redefining healthcare precision and efficiency with artificial intelligence

Kaur, D., & Kaur, P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41545-6.00002-6

AI and Foundation Models in ART: A Medical Engineering Perspective

Liu, M., Guo, F.-Y., & Chang, T. A. (2026) · Fertility & Reproduction · doi:10.1142/s2661318226300059

HistAdvNet: A robust deep learning framework for histopathology classification under adversarial conditions

Islam, R., Hossain, S., & Islam, M. S. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110760

AI Security and Adversarial Defenses

Ramachandran, M. (2026) · Texts in computer science · doi:10.1007/978-3-032-17367-6_17

On generalized hardness of approximation, hallucinations, instability, and trustworthiness in AI for inverse problems

Bastounis, A., & Hansen, A. C. (2026) · Handbook of numerical analysis · doi:10.1016/bs.hna.2026.05.001

Cirriculum-Based Adversarial Training for Robust Medical Imaging Classification

Rajkumar, A. G., & Carberry, J. (2026) · International Conference on Digital Image Processing and Computer Applications (DIPCA) · doi:10.1109/dipca70202.2026.11565976

A quality improvement roadmap for scalable AI implementation in healthcare

Arnaout, A., Abrahams, B., Hinada, P., & McKenzie, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01447-1

MACHINE LEARNING-BASED OBJECTIVE ESTIMATION OF SPINAL PAIN INTENSITY ON THE VISUAL ANALOG SCALE

YILDIZ, Z., Başkurt, F., & Süzen, A. A. (2026) · Mühendislik Bilimleri ve Tasarım Dergisi · doi:10.21923/jesd.1885987

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

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