TIM-7070 v1

Balasubramanian, S., Shukla, V., Islam, N., Upadhyay, A., & Duong, L. (2025). Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic. International Journal of Production Research, 63(2), 594–627. https://doi.org/10.1080/00207543.2023.2263102
This study presents a comprehensive AI framework for healthcare, using the UAE as a case study, and emphasizes AI’s benefits, challenges, and policy needs for resilient health systems.

Siam, A. A., Hassan, M. M., & Bhuiyan, T. (2025). Artificial intelligence for cybersecurity: A state of the art. 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC), AI in Cybersecurity (ICAIC), 2025 IEEE 4th International Conference On, 1–7. https://doi.org/10.1109/ICAIC63015.2025.10848980
This study evaluates AI’s role in cybersecurity by comparing machine learning, deep learning, NLP, and anomaly detection, highlighting their strengths, applications, and potential when combined for enhanced digital security.

Swapna, A., Vemula, H. L., Amarnath, B., & Sasidhar, B. (2024). Leveraging the power of deep learning, machine learning, artificial intelligence, and big data in finance and education. 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS), Technological Advancements in Computational Sciences (ICTACS), 2024 4th International Conference On, 322–327. https://doi.org/10.1109/ICTACS62700.2024.10841229
This study explores how AI, machine learning, deep learning, and big data transform finance and education through automation, personalization, and data-driven decision-making, enhancing performance and outcomes in both sectors.

Sinha, S., & Lee, Y. M. (2024). Challenges with developing and deploying AI models and applications in industrial systems. Discover Artificial Intelligence, 4(1). https://doi.org/10.1007/s44163-024-00151-2
This paper explores the technical, ethical, and regulatory challenges of deploying AI in the industry, offering strategic recommendations for responsible and effective integration of AI applications.

Alhosani, K., & Alhashmi, S. M. (2024). Opportunities, challenges, and benefits of AI innovation in government services: a review. Discover Artificial Intelligence, 4(1). https://doi.org/10.1007/s44163-024-00111-w
This study explores the benefits and challenges of adopting AI in government, using organizational theory to analyze implementation barriers, ethical concerns, and strategies for effective public service delivery.

Sjøvaag, H. (2024). The business of news in the AI economy. AI Magazine, 45(2), 246–255. https://doi.org/10.1002/aaai.12172
This article examines how AI reshapes journalism’s economic foundations, challenging traditional business models and prompting a reconceptualization of competition, platform dynamics, and industry structure.

Da Rocha, H., Chen, G., Li, E., Salvado, J. A., & Espirito-Santo, A. (2025). Smart transducers, robotics and artificial intelligence in Industry 4.0. 2025 IEEE International Conference on Industrial Technology (ICIT), Industrial Technology (ICIT), 2025 IEEE International Conference On, 1–6. https://doi.org/10.1109/ICIT63637.2025.10965221
This paper explores implementing Industry 4.0 technologies—AI, IoT, robotics—in smart factories, highlighting real-time management, interoperability, and standardization to enhance production efficiency and quality.

Lily Ballot Jones, Julia Thornton, & Daswin De Silva. (2025). Limitations of risk-based artificial intelligence regulation: a structuration theory approach. Discover Artificial Intelligence, 5(1), 1–13. https://doi.org/10.1007/s44163-025-00233-9
This article analyzes global AI regulation trends, highlighting the EU AI Act, its limitations, and proposes structuration theory to better address AI’s societal impacts and agency.

Dong, M., Conway, J. R., Bonnefon, J.-F., Shariff, A., & Rahwan, I. (2024). Fears about artificial intelligence across 20 countries and six domains of application. American Psychologist. https://doi.org/10.1037/amp0001454
This study reveals that fears of AI in various occupations are shaped by cultural and psychological factors, suggesting AI adoption strategies must consider global, culturally sensitive perspectives.