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Enhancing Safety in Military Aviation: A Systematic Approach to the Development of AI Certification Standards

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Anatomy of Medical Devices: From Theory to Practice

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A Fine-Tuned Multi-classifier Optimization Framework Towards Safety-Critical Classes

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Advancing Risk Management Processes for Automotive Machine Learning Applications: Optimizing A-SPICE® V4.0 with ISO23894 for Safe and Reliable Deployment

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A Benchmark Suite to Evaluate DNN’s Resilience

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Methodological Pathways for Safety and Risk Mitigation in Reduced Crew Aviation Operations

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Artificial Intelligence on the Shop Floor: A Comprehensive Review of LiteraturePreprint

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Data-Driven and Scenario-Based Risk Analysis Considering Uncertainty in Machine Learning Systems

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Review of Recent Trends in Sensing Methodologies and AI Techniques for Non-Cooperative Aerial Target Recognition

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Integration of Artificial Intelligence in Vehicles and Strategies for Managing the Transition to Electric Vehicles

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Hybrid AI-Hardware Decision Framework for Autonomous Fault Prevention in Extreme Environments: A CubeSat Case Study

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Towards a Domain-Agnostic Safety-by-Design AI Engineering Pipeline

Hoemann, E., Girija, A. A., Christensen, J. M., Kees, Y., Krone, F., Liebert, T., Mut, R., Sauter, G., Stefani, T., Köster, F., et al. (2026) · Lecture notes in business information processing · doi:10.1007/978-3-032-28160-9_24

Certification of AI-Based Aviation Systems: A Methodology for Continuous Safety Assurance Across the System Life Cycle

Schoeman, A., & Panday, A. (2026) · doi:10.3390/engproc2026132007

Detecting intrusions in Internet of Energy (IoE) systems using an automated machine learning framework

Moubayed, A. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100804

Predictive maintenance for additive manufacturing through integrated lean six sigma and artificial intelligence approaches

Kishnani, H., Wang, X. A., Muhammad, S., Yi, S. H., Khurram, M., Garnica, G. J., Abeywardena, K. G., Chadha, U., Srivastava, D., & Kumar, S. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18427-5

Run-Time Assurance for Onboard COTS-Based AI in Safety-Critical LEO Small-Satellite Systems

Khalid, O., Gamazo-Real, J.-C., & Roibás-Millán, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30491-9_28

Explainable Artificial Intelligence: Transitioning DL Model Decisions to User-Understandable Features in Healthcare

Radiuk, P., Barmak, O., Manziuk, E., & Krak, I. V. (2024) · International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI) · doi:10.55056/ceur-ws.org/vol-3777/paper12.pdf

A model-based diagnosis method and application in controllable pitch propellers

Lu, S., Tang, J., Xia, C., Zhang, X., Xu, W., & Li, Z. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.126869

Index policy for condition-based maintenance scheduling of aircraft fleet with limited technicians

Zhu, Y., Li, D., Hu, S., Xia, T., Pan, E., & Xi, L. (2026) · European Journal of Operational Research · doi:10.1016/j.ejor.2026.07.020

Real-Time Vibration Monitoring for Intelligent Vehicles

Aravind, D., & Suresh, N. V. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-8954-7.ch006

Adaptive switching optimization for heterogeneous decision modules under operational uncertainty

Shin, H. K., Lee, C., Cha, J., Kil, M., Choi, J., & C6 (Command, C. (2026) · Journal of Industrial and Management Optimization · doi:10.3934/jimo.2026144

Fault Detectability for Learning-Augmented LPV Systems

Zolghadri, A. (2026) · International Conference on Control, Automation and Diagnosis (ICCAD) · doi:10.1109/iccad69956.2026.11643205

A Systematic Evaluation of Explainable Machine Learning Frameworks for Transparent Decision Support in Safety-Critical Systems

Kumar, S., Verma, P., Khan, U. R., Lakshman, K. N., Nema, P., & Mannepalli, P. K. (2026) · International Conference on Emerging Trends in Information, Communication & Systems (ICETICS) · doi:10.1109/icetics69505.2026.11651305

Utility-grade safety intelligence for thermal power: knowledge graph-augmented multimodal monitoring

Yan, X., Zhou, C., Qian, J., & Ding, Z. (2026) · Journal of Zhejiang University. Science A · doi:10.1631/jzus.a2500399

A Review of TinyML for Human Activity Recognition on Edge Devices

Lamaakal, I., Yahyati, C., Maleh, Y., Makkaoui, K. E., & Ouahbi, I. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8090271 · Cited by 1 · see citing works

AI-enabled dependability diagnostics for industrial product lifecycle management

Rutkowski, I. P., & Nowak, D. W. (2026) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein/235768

What SEs Need to Know about AI and Safety‐Critical Systems

Sheard, S. (2026) · INCOSE International Symposium · doi:10.1002/iis2.70125

A Neuro-Symbolic Hybrid Framework for Automated P&ID Auditing: Integrating Graph Neural Networks with Knowledge-Grounded RAG for Human-in-the-Loop Safety Verification

Shinde, R., Chachad, A. M., & Bhosale, S. S. (2026) · ACS Chemical Health & Safety · doi:10.1021/acs.chas.6c00094

Fault Prediction of Safety Critical Systems using Artificial Intelligence – A Review

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CTX-UXO: Contextual visual dataset for localisation, classification and identification of unexploded ordnance

Craioveanu, M. G., Calofir, V., & Stâmâtescu, G. (2026) · Scientific Data · doi:10.1038/s41597-026-07818-8

Ethical and governance challenges of large language models in power and energy systems: a review and framework for responsible AI deployment

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Introductory Chapter: Evolution of Artificial Intelligence in Medicine and Surgery, from Innovation to Integration

Wojda, T. R., & Stawicki, S. P. (2026) · Artificial intelligence · doi:10.5772/intechopen.1016859

Safety Assurance of Artificial Intelligence-Based Systems: A Systematic Literature Review on the State of the Art and Guidelines for Future Work

Silva, A. V., Camargo, J., Almeida, J., & Cugnasca, P. (2022) · IEEE Access · doi:10.1109/access.2022.3229233 · Cited by 51 · see citing works

Using Artificial Intelligence Techniques in Critical Safety Systems

Rachel, R., & Cugnasca (2006)

A Scenario-Based Method for Safety Certification of Artificial Intelligent Software

Li, G., Lu, M., & Liu, B. (2010) · doi:10.1109/aici.2010.339 · Cited by 1 · see citing works

Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains: A Survey

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A Survey of Artificial Intelligence Approaches to Safety and Mission-Critical Systems

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