Cited by 853Explainable Artificial Intelligence (XAI)Impact of AI and Big Data on Business and SocietyDigital Transformation in Industry

The DOI matches its Crossref record; 83 of the 88 references it cites were found in a registry or an index.

From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

Ahmed, Imran, Jeon, Gwanggil, Piccialli, Francesco·2022·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2022.3146552

Open this paper in CoChat — free account

Abstract

Nowadays, Industry 4.0 can be considered a reality, a paradigm integrating modern technologies and innovations. Artificial intelligence (AI) can be considered the leading component of the industrial transformation enabling intelligent machines to execute tasks autonomously such as self-monitoring, interpretation, diagnosis, and analysis. AI-based methodologies (especially machine learning and deep learning support manufacturers and industries in predicting their maintenance needs and reducing downtime. Explainable artificial intelligence (XAI) studies and designs approaches, algorithms and tools producing human-understandable explanations of AI-based systems information and decisions. This article presents a comprehensive survey of AI and XAI-based methods adopted in the Industry 4.0 scenario. First, we briefly discuss different technologies enabling Industry 4.0. Then, we present an in-depth investigation of the main methods used in the literature: we also provide the details of what, how, why, and where these methods have been applied for Industry 4.0. Furthermore, we illustrate the opportunities and challenges that elicit future research directions toward responsible or human-centric AI and XAI systems, essential for adopting high-stakes industry applications.

Integrity signals

Registry

Crossref · 10.1109/tii.2022.3146552

Cites

83 of 83 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
Industry 4.0 technologies: Implementation patterns in manufacturing companies
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.01.004 · Cited by 2,958
Literature review of Industry 4.0 and related technologies
Oztemel, E., & Gursev, S. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1433-8 · Cited by 2,143
How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.12.031 · Cited by 1,905
The future of manufacturing industry: a strategic roadmap toward Industry 4.0
Ghobakhloo, M. (2018) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-02-2018-0057 · Cited by 1,449
On big data, artificial intelligence and smart cities
Allam, Z., & Dhunny, Z. A. (2019) · Cities · doi:10.1016/j.cities.2019.01.032 · Cited by 1,178
The Role of Additive Manufacturing in the Era of Industry 4.0
Dilberoglu, U. M., Gharehpapagh, B., Yaman, U., & Dölen, M. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.148 · Cited by 1,083

Show all 88 references

Research works printed but not linked: 2

darraj, 0, Artificial intelligence cybersecurity framework: Preparing for the here and now with AI, Proc 18th Eur Conf Cyber Warfare Security Acad Conf Publishing Limited, 132
chawla, 2020, AI, IoT and wearable technology for smart healthcare—A review, Int J Green Energy, 7, 9

Sorted by citations

Items 701–800 of 853

Cluster-Based Fluctuation Counterbalance Enabled Deterministic Resource Scheduling for Industrial Passive Optical Networks
Sang, W., Gu, R., Zhou, Z., Li, H., & Ji, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3604023
“The Industrial Internet mainly focuses on motion/process control, monitoring and measurement, machine vision, digital twins, precise positioning and predictive maintenance applications [3], [4].”
Cloud-assisted high-Sulfur fuel monitoring for connected heavy-duty vehicles based on transformer neural network
Liu, J., Li, Y., Wang, N., Sun, Y. N., Wang, T., Hu, Y., Chen, H., & Gong, X. (2025) · Energy · doi:10.1016/j.energy.2025.138190
Next-Gen Shopping: The Smart Shoppe Trolley for Autonomous Retail Navigation
Agrawal, V., Suryansh, Dwivedi, Y. K., Jitender, Sharma, A., & Maurya, A. K. (2025) · International Conference on Energy, Power and Environment (ICEPE) · doi:10.1109/icepe65965.2025.11139722
HollerithProfitML: A Prototype to support the use and comprehensibility of AI for SMEs
Weiss, L., Möhring, M., Dahal, K. P., & Dagne, S. (2025) · International Conference on Software, Knowledge, Information Management and Applications (SKIMA) · doi:10.1109/skima66621.2025.11155350
“This is crucial to ensure a high acceptance of AI in society and companies of all sizes [12].”
Secure and Scalable Network Security Model Using Reinforcement Learning and Deep Learning Techniques
Palani, S., & Muthukumaravel, A. (2025) · International Conference on Computing Technologies & Data Communication (ICCTDC) · doi:10.1109/icctdc64446.2025.11158768
ROCKET-LRP: Explainable Time Series Classification with Application to Anomaly Prediction in Manufacturing
Ling, Z., Aoyama, T., Yano, K., & Cohen, E. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163765
“While machine learning and deep learning models achieve high accuracy, they often lack explainability, which is crucial in fields like healthcare [2], risk management [25], and manufacturing [26].”
Impact of XAI and Integrated Distributed Deep Learning in Industry 4.0
Dhurgadevi, M., Naveena, N., Easwaramoorthy, S. V., Sugitha, A., & Banupriya, A. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-94637-0_4
Introduction to Industry 4.0: Practical Issues and Challenges
Saranya, S. M., Komarasamy, D., Mohanapriya, S., & Raaju, M. R. P. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-94637-0_1
Convolutional Neural Network-Based Autonomous Perception and Intelligent Decision System for Cabin Cleaning Machines
Yu, X., Huang, Y., Liu, C., & Zhou, S. (2025) · Lecture notes in mechanical engineering · doi:10.1007/978-981-96-5527-4_104
Online Optimization of Reconfiguration Planning for SRMS Based on DQN
Huang, S., Peng, Z., Huang, J., Wang, G., & Yan, Y. (2025) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-00284-6_4
Smart-AM 2025: AI-Driven Innovations for Intelligent Additive Manufacturing in Design, Process Optimization and Quality Assurance
Marco, F. D., Citarella, A. A., & Perna, A. S. (2025) · Biannual Conference of the Italian SIGCHI Chapter (CHItaly) · doi:10.1145/3750069.3757883
“…feedback loops, HCI ensures that decision making remains transparent, adaptive, and user-friendly, even in high-stakes industrial environments [1, 10].AI-augmented AM is therefore not only expected to deliver technical excellence, but also to adapt to dynamic production needs, support…”
UAPFinger: One-to-many Deep Neural Network Fingerprinting via Universal Adversarial Perturbations
Wang, Z., Wu, D., Wang, J., Zhang, X., & Tang, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130048
Quality Control in Plastic Fiber Production: Overcoming Scarce Data with Explainable AI
Ehlenbröker, J.-F., Holst, C., & Lohweg, V. (2025) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa65518.2025.11205754
“Especially in safety-critical applications, such as medical imaging [9]–[11], or in the context of Industry 4.0 [12], where ML is employed for fault detection [13], [14] or as part of Cyber-Security [15], among others, explainability is crucial for ensuring comprehensible and reliable results…”
Influential
Design and Application of a Hybrid Educational Question Answering System Based on Artificial Intelligence Technology
Wei, Y. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-7332-2_38
Harnessing machine learning and artificial intelligence for improved thermal operations
Keshri, S., Patil, U. N., Kumar, K., & Singh, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33881-6.00003-4
XAIUI: User Belief-Driven Explainable AI for Context-Aware Adaptive Interfaces
Langerak, T., Todi, K., Lafreniere, B., Desai, R., & Jonker, T. R. (2025) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3774657 · Cited by 3
“The intersection of adaptive interfaces, AR, and XAI is a growing research area [2].”
Exploration, Learning, and Change: Virtual Ecological Games as Innovative Tools for Environmental Education
Alejandra, B. P. M., Javier, Q. C. Y., Pastora, A. B., & Molero, D. A. L. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-09080-5_33
Expanding the Data Usability for Digital Product Twins
Hakoua, A. N., Ebert, A., Faißt, K.-G., Eickhoff, T., & Göbel, J. C. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-09700-2_1
Rise of Industry 5.0: The Next Frontier of Innovation
Gupta, A., Vishraj, R., Prasad, D., Gupta, S. C., & Nasr, M. M. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815324242125010003
Artificial intelligence in pollution emission control of municipal solid waste incineration process: Status and future prospects
Tang, J., Wang, T., Tian, H., Yu, W., & Qiao, J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26779-6.00009-7
A Multi-Band Self-Attention Network for Motor Imagery Classification
Song, Q., & Kang, G. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254113
“Brain-Computer Interface (BCI) systems provide a new way of human-computer communication by analyzing the subject’s motor state through brain signals and transforming brain thoughts into real commands to control external devices [1].”
Automated Regulatory Compliance Verification for GDPR and ePrivacy Directives Using Hybrid Rule-Based Systems and Explainable AI Models
Ali, F. A. R., Ahmed, S. A., Hasan, M. S., Sahi, S., Samir, M., Mahmod, S. A., & Ahmed, J. M. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292208
FSTAM-net: A Dual Attention Network for Motor Imagery Decoding and Visualization AnalysisPreprint
Fu, S., Shi, B., Wu, D., Yin, S., Liu, G., Zhang, J., Zhu, A., Zhou, G., & Wang, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5615153
Advancements in Predictive Modeling and Neuromodulation for Disorders of Consciousness
Soobia, F., & Qadeer, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-4627-4.ch007
Core Technologies Enabling Industry 5.0
Hammad, M., Ali, H., Hussain, G., & Wang, X. (2026) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-12622-1_3
Homomorphic MaxPooling via Bootstrapping for Privacy-Preserving Neural Networks
Zhang, P., Lu, H., & Ding, T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6209-1_1
Research on the Impact of Generative Artificial Intelligence on Electronic Engineering
Zhang, H., & Nie, C. (2025) · International Conference on Implementing Generative AI into Telecommunication and Digital Innovation · doi:10.1145/3771792.3773856
“In addition, the rise of sensor technology and the Internet of Things (IoT) has expanded the scope of electronic engineering applications, making significant contributions to areas such as smart homes and smart cities (Ahmed , Jeon& Piccialli ,2022).”
Opening the Black Box: The Role of Explainability in AI
Akerkar, R. A. (2026) · Springer eBooks · doi:10.1007/978-3-031-91084-5_14
Artificial Intelligence in the Banking Industry: A Bibliometric and Directions Ahead
Abdeljawad, I., Alia, M. A., Flaifel, S., & Zeidan, S. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-87584-7_3
Decoding Explainable AI: A Critical Review of Emerging Techniques, Key Challenges, and Future Pathways
Hande, Y., Lokhande, J., & Gunjal, A. S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-8895-1_23
Contemporary Aspects in Business Process Modeling/Management
Elstermann, M., Fleischmann, A., Moser, C., Oppl, S., Schmidt, W., & Stary, C. (2026) · Springer eBooks · doi:10.1007/978-3-032-06901-6_4
LogiFusion: a semantic-kinetic-dynamic expert system for closed-loop intralogistics decision intelligence
Zhao, X., He, J., Yang, Y., Yu, J., & Hong, S. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131147
Data-driven discovery of tobacco leaf blending rules for new replacement through integration of thermogravimetric, near-infrared, and attribute-encoded fingerprinting data
Yang, J., Gao, Q., Yang, Q., Jiang, H., Zhang, W., Tang, S., Liu, Z., Chen, A., & Zeng, Z. (2026) · Journal of Analytical and Applied Pyrolysis · doi:10.1016/j.jaap.2026.107611
A Network Security Situation Assessment Scheme With Attack Detection Optimized by TAG-Net
Yang, C., Lan, Y., Ding, Y., Liu, Y., & Liu, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3654622
“Consequently, establishing a resilient cybersecurity defense framework is crucial for timely identiﬁcation, e ﬀ ective response, and ultimate mitigation of the impact of such advanced persistent threats [4].”
Myerson additive explanations on graphs (MYER)
Bhimewar, R., Amit, R. K., & Bharadwaj, P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-343852-3.00018-8
eXplainable Artificial Intelligence for RL-Based Networking Solutions
Calvo, Y. S. M., Rendón, O. M. C., Casas, D. M., & da Fonseca, N. L. S. (2026) · IEEE Communications Magazine · doi:10.1109/mcom.001.2500316 · Cited by 1
“…Local Interpretable Model-Agnostic Ex-planations (LIME), Individual Conditional Expectation (ICE), and Partial Dependence Plot (PDP) allow visualizing internal model processes, analyzing feature relevance, and simplifying models to create more explainable, interpretable, and trustworthy ones [12].”
Cascade systems as an implementation of a gray-box architecture: A case study in traceability for microservice scaling
Garcia, J. A., Lacalle, I., Szmeja, P., Wasielewska-Michniewska, K., Hołda, P., Ganzha, M., Salvador, C. E. P., Bădică, C., Fidanova, S. S., & Paprzycki, M. (2026) · Computer Science and Information Systems · doi:10.2298/csis250425014g
Application of Explainable AI in Behavioral Assessment: From Black-Box Models to Transparent Decision Support Systems
Shan, N., Dong, Z., & Sun, Y. (2025) · International Conference on Software, Systems and Information Technology (SSITCON) · doi:10.1109/ssitcon66133.2025.11341976
“Ahmed et al. [10] conducted a comprehensive survey of Artificial Intelligence and XAI-based methods adopted in Industry 4.0 scenarios.”
Influential
Artificial intelligence tools for preventive maintenance in gold processing mills: A review
Ruvimbo, V. M., Mupona, M. I., & Museka, D. (2025) · i-manager’s Journal on Future Engineering and Technology · doi:10.26634/jfet.21.1.22261
Toward Chiller Condenser Fouling Factor Prediction using Spiking EdgeAI
Linares-Barranco, A., Ávila-Gutiérrez, M., Pérez-Peña, A. M., Montes-Sánchez, J. M., & Lissén, J. M. S. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342629
“[4].”
Adaptive Fractal Representation Learning for Data-Efficient Materials Property Prediction
Qiyang, X., & Jiang, W. (2026) · IEEE Access · doi:10.1109/access.2026.3661003
“Recent developments emphasize explainability and interpretability [10], [11], [12], addressing the critical need for transparent decision-making in scientific applications.”
Su Ürünleri Endüstrisinde Yapay Zekâ Uygulamaları: Sürdürülebilirlik, Verimlilik ve Yenilikçi Çözümler
Utku, E. D. B., & Kutlu, B. (2024) · DergiPark (Istanbul University)
Shifting Industrial Practices to Sustainability
Ahluwalia, R., & Rawat, K. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9554-8.ch009
Ensembled Tabnet-Based Model Approach for Diabetes Disease Classification
Obunge, D. O., Muriira, L. M., & Mbandu, V. (2025) · International Journal of Professional Practice · doi:10.71274/ijpp.v13i4.559
Optimal Tracking Performance of MIMO Cyber‐Physical Systems With Deception Attacks Under Channel Input Power Constraint
Ke, Y., Zhan, X., Wu, J., & Cheng, L. (2026) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.70451
Development of Small Data-Based Mathematical Models for Industrial AI Applications
Hwang, J.-Y., & Oh, H. (2025) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc66702.2025.11389011
Artificial Intelligence and Machine Learning in Advanced Materials Science
Jain, R., Singh, S. K., Upreti, K., & Bakare, Y. B. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-3556-8.ch001
Long-Term Multivariate Forecasting of Quality Variables in Industrial Production Processes Based on a Hybrid Deep Learning Framework and Novel Flexible Sensor
Li, K., Wang, Q., Liu, H., Wang, M., Cui, Y., & Qin, J. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3666071
SIQR: Stochastic games-assisted disclosure of malware propagation in industrial Internet of Things
Shen, S., Wei, S., & Gao, X.-Z. (2026) · Ad Hoc Networks · doi:10.1016/j.adhoc.2026.104191
A novel method of fusing Bert-LSTM and XAI for social media disinformation identification
Xia, H., Islam, N., Zhu, D. L., Zhang, Z., Behl, A., & Roohanifar, M. (2026) · Annals of Operations Research · doi:10.1007/s10479-026-07063-7 · Cited by 1
Implementation of In-Situ Vision System on Transfer Line to Improve Packaging Quality and Minimize Waste: Automotive Supplier Case Study
Dahake, V. M., Pawar, U., Bhole, K. S., Bhole, D., & Rathod, M. (2026) · Journal of The Institution of Engineers (India) Series C · doi:10.1007/s40032-026-01344-7
Towards Quantum-Driven Multimodal Machine Learning: Methods, Challenges, and Future Directions
Das, D., Claiborne, J., Brinkley-Tapp, L., Houston, M., Chatterjee, P., & Ghosh, U. (2025) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa67132.2025.00071
“Systems using AI can already recognize images, translate languages, and make predictions from data at scales unthinkable only a decade ago [1].”
TAAGNet: a temporal–attribute–adaptive graph convolutional network for unsupervised cross-domain bearing fault diagnosis
Zhou, R., Jiang, H. K., Zhang, X., & Hu, X. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae4cb2
AI-Driven Predictive Grid Maintenance for Reducing Supply Chain Delays in Utility Spare-Parts Logistics
Jimoh, M., EKWUNIFE, D. I., Ojo, S., & Gbolade, O. (2023) · International Journal of Scientific Research and Modern Technology. · doi:10.38124/ijsrmt.v2i11.1267
49Soft Computing Theory and Applications Related to Green Manufacturing
Kumar, S., Singh, N. K., & Singh, Y. (2025) · De Gruyter eBooks · doi:10.1515/9783111631509-003
Integrating Machine Learning and Explainable AI for Decision Support in Supply Chain Management
Huq, M. R., Isty, M. N., Gani, R., Islam, T., & Ahmed, J. (2026) · Journal of Engineering · doi:10.1155/je/4839375
Task Chain-Oriented Trajectory and Resource Optimization in UAV-Assisted Multi-Agent Wireless Networks via Cognitively Informed Reinforcement Learning
Ren, Y., Lv, Z., Zhang, H., Yu, F. R., Cao, X., & Li, L. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3676419
“T HE rapid convergence of mobile communications, Internet of things (IoT) architectures [1], and artiﬁcial intelligence (AI) techniques [2] has enabled large-scale deployments of intelligent ground devices with sensing, data acquisition, and local inference capabilities.”
Learning from practice: How professionals use AI and what it means for higher education
Christian, E. N., Jia, X., Dellen, S. A. V., Zubovic, Y., & Dehr, K. (2026) · Industry and Higher Education · doi:10.1177/09504222261438628
Artificial Intelligence for Industrial Efficiency
Amala, K. J., Rajeswari, D., & Venkatachalam, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5946-5.ch006
Deep learning-based framework for automated circuit waveform anomaly detection in intelligent manufacturing
Hsu, H.-M., Chang, K.-L., Chang, W.-C., Lee, C.-H., Hsueh, H.-W., Tseng, C.-W., & Hsu, S.-H. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-17900-5
“These digital technologies include the application of artificial intelligence for measurement data analysis [16, 17].”
The role of artificial intelligence in Industry 4.0
Pourrahmani, H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45526-1.00013-1
Multi-Condition Vehicle Body Optimization via Reinforcement Learning from Human Feedback with Policy TransferPreprint
Li, Z., Sun, Z., Jia, L., Hao, J., & Ran, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6550923
Unified adaptive open-set incremental learning for industrial human action recognition
Yu, B., Wang, P., & Yan, J. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.04.021
"Do I Really Need This?": Illuminating Challenges in Integrating Computational Training Tools in Esports Coaching
Kleinman, E., Kim, S., Kim, S., Lee, H., Kim, J., Kang, D., Park, S., Harteveld, C., & Lee, B. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791065
“Insights from domains such as explainable AI [3] and personal health informatics [34], which develop technology meant to be trustworthy and under-standable to both experts and lay people, may help.”
Explainable AI Framework for Big Data–Driven Decision–Making in Smart Cities of Industry 4.0
Xu, N., & Wang, Q. (2026) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213026400087
Wireless transmission adaptive safety valve monitoring system based on machine learning
Han, M., & Liu, X. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-11968-6
“At the same time, Ahmed et al. [25] conducted a systematic study on artificial intelligence and explainable artificial intelligence (XAI) in the Industry 4.0 scenario, summarized the adaptation paths and challenges of current algorithms in high-risk industrial scenarios, and pointed out that…”
Influential
Explainable Lightweight Deep Learning-Based Intrusion Detection for Healthcare 4.0 Using Biomedical and Network Signals
Salman, O. S., Faraj, A. A., Abdulraheem, M. M., Al-Barzinji, S. M., & Abdlhamed, A. A. (2026) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses66558.2026.11479149
“Researchers study pharmaceutical supply chains [6], the integration of blockchain technology with IoT infrastructure to improve road health [7], and issues that indirectly affect patients, such as hospital electricity management, which could improve service and patient well-being.”
HITE: A Hierarchical Intrinsically Interpretable Trust Evaluation Framework for Industrial IoT
Zhang, M., Cheng, Y., Xi, C., Wang, H., & Cui, T. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3686701
“Therefore, there is an urgent industrial need for a trust evaluation framework that reconciles this tradeo ﬀ , maintaining the high accuracy of DL while providing the end-to-end transparent reasoning typical of intrinsic models [18].”
LLM-driven human-AI collaborative decision support system for complex industrial processes: A case study in metallurgy
Zong, Y., Jia, R., Li, K., Xue, D., Zhang, L., & He, D. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109055 · Cited by 2
Explainable AI Techniques for Quality Improvement in Automotive Manufacturing Processes
Dacal-Nieto, Á., Vicente-Estévez, D., Raña, B., Paradela, A., Bugarín, A., Areal, J. J., & Alonso-Ramos, V. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-18802-1_7
Rate-Distortion Trade-Off in Integrated Communication, Sensing, and Control Closed-Loop Systems
Fan, Y., Feng, Z., Meng, Z., Wei, Z., Zhou, Y., & Niu, K. (2026) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2026.3690330
Construction of a Network Security Situation Awareness Model Based on Artificial Intelligence
Zhang, X., Wang, J., Fang, R., Guo, K., & Chen, W. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-0788-7_51
Advancements and Applications of Explainable Artificial Intelligence in Industry 4.0
Mathivanan, S., Sarumathi, S., Phu, V. T., Saraswathy, C., Sivasundaram, M., & Karpagam, M. (2026) · doi:10.1002/9781394305612.ch12
An Advanced AI Framework for Pharmaceutical Drug Identification Based on Attention-Driven Vision Transformers and Evidential Neural Networks
Brindha, M., Dhivya, R., Kavya, M., Madhumitha, T., & Menaka, S. (2026) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs67901.2026.11502640 · Cited by 1
“The paper is an extensive overview of AI and XAI-based solutions implemented in the Industry 4.0 case [14 -15].”
Influential
Application of artificial intelligence in steelmaking processes: A state-of-the-art review in the context of industry 4.0
Dai, Z., Xu, Y., & Wang, Y. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18073-x
“Under this paradigm, production systems are expected to achieve real-time perception, interconnected decision-making, and adaptive optimization, thereby improving productivity, flex - ibility, and resource efficiency [ 2–6 ].”
Explainable Artificial Intelligence (XAI) in IoT and WSN Security: A Systematic Review
Jain, K., Kumar, S., Sapra, V., & Agarwal, A. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10623-6
Enhancing Energy Efficiency in Underground Coal Mines Through IoT and AI Integration
Roy, M. S., Sanyal, P., Banerjee, P., Singha, T., & Mandal, B. K. (2026) · Journal of The Institution of Engineers (India) Series D · doi:10.1007/s40033-026-01021-0
Understanding 5G From a Safety-Critical Perspective: How to Properly Trust Wireless Networks Through a Dependable-Aware Networking Service
Souza, J. L. R., & Siqueira, F. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112353
Bridging behavior and brain: the impact of emotional and linguistic cues on human performance and neural activation
Qu, X., Cai, Y., & Rau, P. P. (2026) · Ergonomics · doi:10.1080/00140139.2026.2670660
KI und Skalenentwicklung im Marketing – ist Vorsicht geboten?
Walsh, G., Stichnoth, K., & Wilczak, M.-A. V. (2026) · Springer eBooks · doi:10.1007/978-3-658-49371-4_14
Human-Controllable AI: Meaningful Human Control
Liu, C., & Xu, W. (2026) · Springer eBooks · doi:10.1007/978-981-95-3658-0_58
A physics-informed collaborative interpretable framework for wind turbine anomaly detection
Zhu, J., Chen, W., Su, L., Lan, B., Pei, T., & Jin, L. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100773
Toward reliable hazardous gas identification: A verifiable and interpretable deep learning framework with quantified explanations
Zhu, Z., Chen, J., He, Q., Xia, X., Wu, P., Peng, X., & Chu, J. (2026) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2026.109032
Open-set domain generalization residual feature reconstruction network for intelligent fault diagnosis under unknown time-varying working conditions
Liu, J., Zhang, X., Jiang, H., Yuan, Y., & Sun, K. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.114459
Development and Future Research Directions of AI-Based Anomaly Detection in Smart Manufacturing: A Bibliometric Analysis
Nebel, M., Stahmann, P., & Janiesch, C. (2026) · Lecture notes in information systems and organisation · doi:10.1007/978-3-032-08483-5_24
Insider Threat Detection System with Federated Learning and Differential Privacy
Samuel, J. J. G., Balasubramaniya, V., Kumar, G. G., Geetha, R., & Shobana, R. (2026) · International Conference on Intelligent Computing, Networks, and Security (IC-ICNS) · doi:10.1109/ic-icns68863.2026.11537834
Semantic-Aware Multimodal Optimization and Policy Learning for Intelligent Product Design Systems
Xue, F., & Xue, F. (2026) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.411888
Optimizing Industrial Quality Control through Explainable Artificial Intelligence
Saati, H., Hailouch, H. E., Chakir, H. E., Khaili, M. E., & Nhaila, H. (2026) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset68627.2026.11538464
Leveraging Frequency-division Fusion for Enhanced EEG Signal Processing and Model Optimization in Brain-Computer Interfaces
Liu, C., Luo, Y., (7081), W. L., Gan, Z., Zhang, L., & Kang, X. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110784
RePriChain: A Privacy-Enhanced Blockchain With Dynamic Reputation Management for Industry 4.0 Supply Chains
Hao, Y., Wang, J., Li, Q., Liu, L., Zhang, Z., & Yang, H. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3682298
“W ITH the rise of Industry 4.0, the integration of advanced technologies such as automation, enhanced connectivity, the Internet of Things (IoT), and Artiﬁcial Intelligence (AI) is redeﬁning supply chain management[1].”
Influential
Digital twins assisted for fault diagnosis of bearing: a literature review
Xing, S., Ma, H., Yang, X., Ju, F., & Wang, J. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae7c9c · Cited by 1
Joint Optimization of Feeder Allocation and Pickup Scheduling in Pcb Assembly Via Cooperative Multi-Agent Learning
Yin, B., Liu, Z., Yang, X., & Lu, G. (2026) · Conference on Fully Actuated System Theory and Applications (FASTA) · doi:10.1109/fasta70174.2026.11548724
“In the context of Industry 4.0, artificial intelligence (AI) has emerged as a critical enabler for this industrial evolution [2, 3].”
Influential
Intelligent measurement method for minimum safety distance in distribution network operations based on binocular stereo perception and spatial point cloud clustering
Ma, F., Yu, X., Wang, B., Mohamed, M. A., & Alharbi, A. M. (2026) · Measurement · doi:10.1016/j.measurement.2026.122274
PILSTM-PIT: A Multimodal Model for Forecasting Fluctuating Soil Temperature Measurement Data
Rui, S., Wang, Y., Xu, S., Qiao, Z., Wang, Y., Wang, L., Shi, Y., & Cai, M. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3704173
“From this perspective, autoML-style hyperparameter search [54] may reduce manual tuning bias when the model is transferred across remediation sites with di ﬀ erent thermal conditions, while explainable AI [55] can help engineers understand which historical intervals and which sensor variables…”
Privacy-Aware Retrieval-Augmented Generation for Intelligent IoMT Healthcare Systems
Yogi, M. K., & Donga, D. (2026) · Journal of Ubiquitous Computing and Communication Technologies · doi:10.36548/jucct.2026.3.002
“…is becoming increasingly digitized with a revolutionary approach enabled by Internet of Medical Things (IoMT) – a network of connected sensors, implantable devices, wearable monitors and smart health applications that electronically create and transmit real-time patient health data [1].”
The Evolving Role of Human Intelligence in Industrial AI Adoption
Kumar, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7036-1.ch002
Organising Flexibility in Generative AI-Mediated Work: Evidence from B2B Sales–Procurement
Malhan, M. (2026) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-026-00502-1
“Speciﬁcally, liquidity synthe-sises prior emphasis on interoperability, lineage, coverage, and latency drawn from digital transformation, Industry 4.0, and information systems research (Ahmed et al., 2022; Piccoli et al., 2022; Schmidt et al., 2023).”
Influential
Preaching Meaning, Practicing the Motions: A Critical Analysis of XAI Research
van der Linden, I., & Ghebreab, S. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812356
From Explanation to Clinical Feasibility: A Systematic Review and Integrated Conceptual Framework of Explainable AI, Counterfactual Reasoning, and Human-Machine Teaming in Stroke Decision Support (Preprint)Preprint
Liu, Z., & Sharef, N. M. (2026) · doi:10.2196/preprints.103492
Explainable Artificial Intelligence and sustainable computing in Machine Learning
Galal, M. K., & Said, Z. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33943-1.00010-1

Authors: Ahmed, I., Jeon, G., & Piccialli, F.
Affiliations: Institute of Management Sciences Peshawar (PK)
Incheon National University (KR)
University of Naples Federico II (IT)
ORCID: Ahmed, Imran
Jeon, Gwanggil
Piccialli, Francesco
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-08
Volume: 18
Issue: 8
Pages: 5031-5042
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2022.3146552
OpenAlex: W4210725851
Cited by: 853
References: 88
Citations by year: 2026: 163 · 2025: 246 · 2024: 249 · 2023: 149 · 2022: 46
FWCI: 56.77
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Industrial Informatics (publishedVersion)
Funders: 4I: mixed reality, machine learning, gamification and educational for Industry (F/190130/01-03/X44)
Fondo per la Crescita Sostenibile—Sportello
Fabbrica Intelligente
PON I & C (CUP: B66G21000040005, COR: 4641138)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
Keywords: explainable AI, Industry 4.0, machine learning, deep learning, predictive maintenance, downtime reduction, responsible AI, human-centered AI, intelligent machines, industrial transformation

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

From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks