Abdolahi Shahvali, E., Arizavi, Z., Hemmatipour, A., & Jahahgirimehr, A. (2024). Investigating the level of knowledge, attitude, and performance of students regarding the applications of artificial intelligence in nursing.
Jundishapur Scientific Medical Journal (JSMJ),
23(2), 134–142.
https://doi.org/10.32592/JSMJ.23.2.134
Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets.
Journal of Political Economy,
128(6), 2188–2244.
https://doi.org/10.1086/705716
Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation.
Journal of Economic Perspectives,
29(3), 3–30.
https://doi.org/10.1257/jep.29.3.3
Ayesha, Noor ul Amin, M., Albalawi, O., Mushtaq, N., Mahmoud, E. E., Yasmeen, U., & Nabi, M. (2024). Modeling health outcomes of air pollution in the Middle East using support vector machines and neural networks.
Scientific Reports,
14, Article 21517.
https://doi.org/10.1038/s41598-024-71694-8
Bogen, M., & Rieke, A. (2018). Help wanted: An examination of hiring algorithms, equity, and bias. Upturn.
Briggs, J., & Kodnani, D. (2023). The potentially large effects of artificial intelligence on economic growth. Goldman Sachs Global Economics Analyst.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
Brynjolfsson, E., Rock, D., & Syverson, C. (2018).
Artificial intelligence and the modern productivity paradox (NBER Working Paper No. 24001). National Bureau of Economic Research.
https://doi.org/10.3386/w24001
Cazzaniga, M., Jaumotte, F., Li, L., Melina, G., Panton, A., Pizzinelli, C., Rockall, E., & Tavares, M. (2024). Gen AI: Artificial intelligence and the future of work. International Monetary Fund.
Conboy, K., Mikalef, P., Dennehy, D., & Krogstie, J. (2020). Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda.
European Journal of Operational Research,
281(3), 656–672.
https://doi.org/10.1016/j.ejor.2019.06.051
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023).
GPTs are GPTs: An early look at the labor market impact potential of large language models [arXiv preprint].
https://doi.org/10.48550/arXiv.2303.10130
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks.
Nature,
542(7639), 115–118.
https://doi.org/10.1038/nature21056
Fan, D., Miao, R., Huang, H., Wang, X., Li, S., Huang, Q., & Deng, R. (2024). Multimodal ischemic stroke recurrence prediction model based on capsule neural network and support vector machine.
Medicine,
103, Article e39217.
https://doi.org/10.1097/MD.0000000000039217
Filip, R., Gheorghita Puscaselu, R., Anchidin Norocel, L., Dimian, M., & Savage, W. K. (2022). Global challenges to public health care systems during the COVID-19 pandemic: A review of pandemic measures and problems.
Journal of Personalized Medicine,
12(8), Article 1295.
https://doi.org/10.3390/jpm12081295
Gifford, R., van de Baan, F., Westra, D., Ruwaard, D., & Fleuren, B. (2023). Through the looking glass: Confronting health care management’s biggest challenges in the wake of a crisis.
Health Care Management Review, 48(2), 185–196.
https://doi.org/10.1097/HMR.0000000000000365
Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering epidemic outbreaks: A case study of COVID‑19.
Transportation Research Part E: Logistics and Transportation Review, 138, 101967.
https://doi.org/10.1016/j.tre.2020.101967
Guo, Z. X., & Wong, W. K. (2013). Fundamentals of artificial intelligence techniques for apparel management applications. In
Optimizing decision making in the apparel supply chain using artificial intelligence (AI): From production to retail (pp. 13–40). Woodhead Publishing.
https://doi.org/10.1533/9780857097842.13
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2022). The practical implementation of artificial intelligence technologies in medicine.
Nature Medicine,
28, 31–38.
https://doi.org/10.1038/s41591-021-01614-0
Hosseini Ramsheh, L. S., Zandi Isfahan, S., & Shokoohi Ghahfarkhi, G. (2025). Review of the application of artificial intelligence systems in disease diagnosis. In
First International Conference on Health, Hygiene and Education. Civilica.
https://civilica.com/doc/2084814
Hosseini, M. H., & Mohseni Fard, N. (2015). The effect of artificial intelligence on the purchase intention of insurance industry customers with the mediating role of customer experience and interaction.
Scientific Journal of New Research Approaches to Management and Accounting,
8(31), 1995–2009.
https://majournal.ir/index.php/ma/article/view/3072
Iqbal, T., Masud, M., Amin, B., Feely, C., Faherty, M., Jones, T., & Vazquez, P. (2024). Towards integration of artificial intelligence into medical devices as a real‑time recommender system for personalised healthcare: State‑of‑the‑art and future prospects.
Health Sciences Review, 10, 100150.
https://doi.org/10.1016/j.hsr.2024.100150
Kok, J. N., Boers, E. J., Kosters, W. A., Van der Putten, P., & Poel, M. (2009). Artificial intelligence: definition, trends, techniques, and cases. Artificial intelligence, 1(270-299), 51.
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine.
Journal of the American College of Cardiology, 69(21), 2657–2664.
https://doi.org/10.1016/j.jacc.2017.07.723
Kumar, V. V., Sahoo, A., Balasubramanian, S. K., & Gholston, S. (2025). Mitigating healthcare supply chain challenges under disaster conditions: a holistic AI-based analysis of social media data.
International journal of production research,
63(2), 779-797..
https://doi.org/10.1080/00207543.2024.2316884
Machucho, L., & Ortiz, J. (2025). Ethical and strategic implications of AI in business innovation. Journal of Business Ethics and AI, 6(2), 144–163. https://doi.org/10.3390/jpm12081295
Mirmasoumi, M. (2023). Review of recent advances in artificial intelligence in healthcare and medicine based on systematic sources. Intelligent Knowledge Exploration and Processing, 4(10), 70-81
Moradzadeh, M., Karamouzian, M., Najafizadeh, S., Yazdi-Feyzabadi, V., & Haghdoost, A. A. (2023). International Journal of Health Policy and Management (IJHPM): A Decade of Advancing Knowledge and Influencing Global Health Policy (2013-2023).
International Journal of Health Policy and Management,
12, 8124.
https://doi.org/10.34172/ijhpm.2023.8124
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review.
The Lancet Digital Health, 2(8), e375–e383.
https://doi.org/10.1016/S2589-7500(20)30054-6
Najafi, O. A. (2025). Predicting heart disease with artificial intelligence: A model for industrial efficiency. In
Third National Conference of the Student Branch of Modern Management in the Age of Artificial Intelligence. Civilica.
https://civilica.com/doc/2350300
Qasemi, R., & Mohammad Taghavi, S. A. (2025). Evaluating the effects of artificial intelligence performance in current and future medicine. In
Twenty-fourth National Conference on Electrical, Computer and Mechanical Engineering. Civilica.
https://civilica.com/doc/2159273
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., Mossin, A., … Dean, J. (2018). Scalable and accurate deep learning with electronic health records.
NPJ Digital Medicine,
1, Article 18.
https://doi.org/10.1038/s41746-018-0029-1
Rouhani, Z. (2025). Investigation of the effects of artificial intelligence on the optimization of industrial processes. In
Seventh Annual National Congress for the Development of Modern Sciences and Technologies of Iran. Civilica.
https://civilica.com/doc/2505534
Sallam, M., Shukar, S., Babar, Z. U. D., Hernández, A. F., Hossain, A., Saleem, F., Kibuule, D., & Alrasheedy, A. A. (2021). Drug shortage: Causes, impact, and mitigation strategies.
Frontiers in Pharmacology,
12, Article 693426.
https://doi.org/10.3389/fphar.2021.693426
Shafa, M. (2025). AI-powered business intelligence and organizational decision-making: A PRISMA-based review.
Journal of Strategic Information Systems,
34(2), Article 101787.
https://doi.org/10.1016/j.jsis.2024.101787
Vahedi, H., & Shirazi, S. (2025). Artificial intelligence-based sales in industrial marketing. In
Fourth International Conference on Architecture, Civil Engineering, Urban Planning, Environment and Horizons of Islamic Art in the Second Step of the Revolution. Civilica.
https://civilica.com/doc/2428633
World Economic Forum. (2023). The future of jobs report 2023. World Economic Forum.
Yang, G.‑Z., Cambias, J., Cleary, K., Daimler, E., Drake, J., Dupont, P. E., Hata, N., Kazanzides, P., Martel, S., Patel, R. V., Taylor, R. H., Tsekos, N. V., & Troccaz, J. (2017). Medical robotics—Regulatory, ethical, and legal considerations for increasing levels of autonomy.
International Journal of Computer Assisted Radiology and Surgery, 12(10), 1829–1836.
https://doi.org/10.1007/s11548-017-1591-1
Jafari, M., Akhavan, P., & Akbari, A. H. (2026). Enhancing supply chain agility and performance through big data analytics: the role of digitalization and top management support. International Journal of Productivity and Performance Management, 1-22.
https://doi.org/10.1108/IJPPM-06-2025-0557
Tavakkoli-Moghaddam, R., Akbari, A. H., Tanhaeean, M., Moghdani, R., Gholian-Jouybari, F., & Hajiaghaei-Keshteli, M. (2024). Multi-objective boxing match algorithm for multi-objective optimization problems. Expert Systems with Applications, 239, 122394.
https://doi.org/10.1016/j.eswa.2023.122394