There has been much focus toward increasing integration of AI in healthcare generally, with a specific focus on inventory management systems in particular. As more hospitals in the United States public and private side face increasing costs and concerns over cost and length of operation all systems supporting inventory must be made most efficient and AI system offers the US hospitals the opportunity to maintain accurate inventory and hence improve the quality of patient care. But the literature review presents a small number of studies that focus on the effects of these systems as the means to improve healthcare results and organization. In this study, it is proposed that improving inventory management by actioning artificial intelligence in healthcare organizations will result in enhanced patient care and improved inventory accuracy, cost reduction and supply chain efficiency in the United States of America’s healthcare institutions. A cross-sectional, quantitative survey was administrated to 200 healthcare personnel and supply chain managers from different healthcare organizations based on hospitals, clinics and specialty medical centers of the USA. Sample design: Participants were purposefully chosen, who had prior exposure to the AI-induced systems or are aware about workflow of such systems. Respondents’ data was gathered via an online questionnaire and analyzed using SPSS: descriptive analysis, chi-square test, one-way ANOVA, correlation analysis, multiple regression analysis was used to establish the relationships of the study variables: AI systems, healthcare outcomes and supply chain performance.

The results showed that the use of AI in inventory management systems enhances its accuracy in ordering order itself and overall patient care. Separate studies showed that where the management of a facility has deeper insight of the AI system then there will be more operations worked on to improve efficiency or cut costs. Adoption of these pathways among smaller providers including clinics and long-term care facilities was difficult, especially due to cost constraints. An analysis of the multiple regression equation showed that knowledge of AI improved the projection of potential healthcare outcome, creating greater worth and also increases the performance of supply chain. The outcomes of using AI based inventory management systems in the United States seem to have a lot of advantages that may help improve the efficacy of operations in the healthcare system. Issues such as funding and skills needed for implementation, especially in small healthcare organization, continue to slow the uptake of the technology. These existing problems may be solved if more focused investments in the development of comprehensive AI training or systematic solutions are given to enable the healthcare industry to reach the full potential of the application of artificial intelligence.

Artificial Intelligence, Inventory Management, Healthcare Outcomes, Supply Chain Performance

Arshad, N., Baber, M. U., & Ullah, A. (2024). Assessing the Transformative Influence of ChatGPT on Research Practices among Scholars in Pakistan. Mesopotamian Journal of Big Data, 2024, 1-10.

Chen, X, & Li, J. (2022). AI adoption in healthcare supply chains: Challenges and opportunities. Journal of Healthcare Management, 68(1), 45-59. https://doi.org/10.1016/j.jhcm.2022.10.004.

Chen, Y, Huang, X, & Zhou, Z. (2022). Artificial intelligence in healthcare: Applications, challenges and opportunities. Healthcare Informatics Review, 29(1), 45-62. https://doi.org/10.1016/j.hcin.2022.05.002.

Davenport, T, & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94.

Ghassemi, M, Oakden-Rayner, L, & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in healthcare. The Lancet Digital Health, 3(11), e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9.

Guha, S, Kumar, P, & Pattnaik, C. (2021). Predictive analytics and AI in supply chain management: A systematic review. Journal of Business Research, 128, 62–75. https://doi.org/10.1016/j.jbusres.2021.01.041.

He, J, Baxter, S. L, Xu, J, Xu, J, Zhou, X, & Zhang, K. (2020). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25, 30–36. https://doi.org/10.1038/s41591-018-0307-0.

Ivanov, D, & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 31(2–3), 198–211. https://doi.org/10.1080/09537287.2019.1640706.

Johnson, K. W, Torres Soto, J, & Glicksberg, B. S. (2022). Artificial intelligence in healthcare: The hope, the hype, the promise, the peril. Nature Medicine, 28(1), 44-50. https://doi.org/10.1038/s41591-021-01614-0.

Kaplan, A, & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004.

Lee, S, Kim, D, & Park, J. (2023). Machine learning applications in healthcare inventory management: A systematic review. Healthcare Management Review, 45(2), 78–93. https://doi.org/10.1097/HMR.2023.123.

Liu, Y, & Zhang, W. (2023). Leveraging artificial intelligence for efficient healthcare supply chains: A case study in the U.S. International Journal of Healthcare Technology, 27(3), 112–124. https://doi.org/10.1016/j.ijht.2023.03.005.

Mehta, V, Rajan, A, & Patel, P. (2023). AI-driven inventory systems and their impact on healthcare operations: A comparative study. Journal of Operations in Healthcare, 15(2), 150–170. https://doi.org/10.1080/14780000.2023.789.

Nishan, A., Raju, S. T. U., Hossain, M. I., Dipto, S. A., Uddin, S. T., Sijan, A., ... & Khan, M. M. H. (2024). A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms. Heliyon, 10(6).

Prause, G, & Weigand, J. (2020). Smart logistics in Industry 4.0: A quantum leap in supply chain transparency and efficiency. International Journal of Production Research, 58(10), 2972–2982. https://doi.org/10.1080/00207543.2020.1717931

Rahman, S., Alve, S. E., Islam, M. S., Dutta, S., Islam, M. M., Ahmed, A., ... & Kamruzzaman, M. (2024). UNDERSTANDING THE ROLE OF ENHANCED PUBLIC HEALTH MONITORING SYSTEMS: A SURVEY ON TECHNOLOGICAL INTEGRATION AND PUBLIC HEALTH BENEFITS. Frontline Marketing, Management and Economics Journal, 4(10), 16-49.

Rahman, S., Islam, M., Hossain, I., & Ahmed, A. (2024). THE ROLE OF AI AND BUSINESS INTELLIGENCE IN TRANSFORMING ORGANIZATIONAL RISK MANAGEMENT. International journal of business and management sciences, 4(09), 7-31.

Rahman, S., Islam, M., Hossain, I., & Ahmed, A. (2024). UTILIZING AI AND DATA ANALYTICS FOR OPTIMIZING RESOURCE ALLOCATION IN SMART CITIES: A US BASED STUDY. International journal of artificial intelligence, 4(07), 70-95.

Raju, S. T. U., Dipto, S. A., Hossain, M. I., Chowdhury, M. A. S., Haque, F., Nashrah, A. T. ... & Hashem, M. M. A. (2024). DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model. Medical & Biological Engineering & Computing, 1-22.

Raju, S. T. U., Dipto, S. A., Hossain, M. I., Chowdhury, M. A. S., Haque, F., Nashrah, A. T., ... & Hashem, M. M. A. (2023). A Novel Technique for Continuous Blood Pressure Estimation from Optimal Feature Set of PPG Signal Using Deep Learning Approach.

Reddy, S, Fox, J, & Purohit, M. P. (2020). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 113(1), 14–20. https://doi.org/10.1177/0141076819877558

Sayem, M. A., Taslima, N., Sidhu, G. S., Chowdhury, F., Sumi, S. M., Anwar, A. S., & Rowshon, M. (2023). AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices. Int. J. Recent Innov. Trends Comput. Commun, 11(10), 1109-1122.

Shabbir, A., Arshad, N., Rahman, S., Sayem, M. A., & Chowdhury, F. (2024). Analyzing Surveillance Videos in Real-Time using AI-Powered Deep Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 12(2), 950-960.

Srinivas, K, Ghosh, A, & Basu, S. (2023). AI and machine learning in hospital supply chain management: Current trends and future directions. Journal of Medical Systems, 48(1), 1–15. https://doi.org/10.1007/s10916-023-1789-2.

Taylor, R, James, P, & Anderson, M. (2022). The role of AI in mitigating supply chain disruptions in healthcare: A review of strategies and applications. Healthcare Supply Chain Review, 17(3), 245–260. https://doi.org/10.1080/01972456.2022.325

Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

Ullah, A., Shahzad, F., Ahmad, W., & Naseer, M. (2024). MOBILE PERSONAL INFORMATION MANAGEMENT SKILLS OF THE UNIVERSITY LIBRARIANS IN PAKISTAN. Remittances Review, 9(S2 (May 2024)), 405-431.

Ullah, W., Usman, M., & Ullah, A. (2024). Usage of E-Resources Among the Students of GCUF Library. International Journal of Scientific Multidisciplinary Research, 2(2), 153-168.

Wang, H, Zhao, L, & Chen, M. (2023). The integration of AI and healthcare logistics: Challenges and future prospects. Journal of Healthcare Logistics, 16(4), 187–204. https://doi.org/10.1016/j.jhl.2023.10.013.

Wong, W. K, Chan, T. M, & Selvadurai, M. (2020). AI-powered supply chains: A leap into the future. International Journal of Production Research, 58(13), 3900–3916. https://doi.org/10.1080/00207543.2019.1646328.

Zhou, K, & Xiao, S. (2023). Artificial intelligence in healthcare supply chains: Impact on cost, efficiency and patient outcomes. Healthcare Technology Management, 8(2), 100–115. https://doi.org/10.1002/htm.202345.