Real-Time Decisioning Across Health, Business, and Finance
Keywords:
Artificial Intelligence, Automated Decisioning, Data Fabric, Data Governance, Data Privacy, Data Quality, Enterprise AI, Financial Services, Healthcare, Privacy-Preserving Analytics, Real-Time Analytics, Risk Management, Streaming Analytics, Trust Frameworks.Abstract
A unified intelligent data fabric enables real-time decisioning across rapidly evolving healthcare, enterprise AI, and financial ecosystems. The growing body of services and products in these domains—often developed in silos but requiring coherent integration—creates onerous operational and administrative overheads. Real-time decisioning capitalizes on investment in a data fabric to provide cohesive responses across data domains with minimal additional cost. Architectural principles that underpin the data fabric also govern cross-domain decisioning: modularity, interoperability, latency awareness, and governance-by-design. These qualities enable transparent integration of shared components into an end-to-end decisioning flow with comprehensive management.
Real-time decisioning across these ecosystems poses specific challenges. Healthcare centers on analytics and decision-support tools that empower patients and their families. In the enterprise, latent models powered by data lineage and continuous learning supervise and improve data-driven automation. In the financial domain, low-latency connections verify trades and positions, sustain anti-fraud vigilance, communicate with customers, satisfy compliance requirements, and mitigate risk. Addressing these challenges establishes a foundation for additional domains, such as telecommunications and online gaming.
References
1. Ahmad, T., Haroon, M. B., Hui, J., Khan, M., & Memon, S. (2021). Data-driven healthcare decision making: Machine learning and big data analytics. Journal of Biomedical Informatics, 121, 103876.
2. Benjamens, S., Dhunnoo, P., & Meskó, B. (2021). The state of artificial intelligence-based FDA-approved medical devices and algorithms. NPJ Digital Medicine, 4(1), 1–8.
3. Amistapuram, K., Pandiri, L., Raju, V. R., Paleti, S., Singireddy, S., & Sheelam, G. K. (2025). AI-Based Cloud Infrastructure and MLOps Frameworks for Scalable Data Engineering Across Banking and Insurance. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 186–192). IEEE. 2025 IEEE International Conference on Communication Networks and Computing (CNC). https://doi.org/10.1109/cnc68716.2025.11484532
4. Cao, L. (2021). AI in finance: Challenges, techniques and opportunities. ACM Computing Surveys, 55(3), 1–38.
5. Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2021). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 49(1), 24–42.
6. Danghi, P. S., Maniraj, K., Jain, P., Adilakshmi, K., Garapati, R. S., & Jain, S. K. (2025). Artificial Intelligence Based Energy Optimization Framework for Wireless Sensor Networks. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1–6). IEEE. 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG). https://doi.org/10.1109/ictbig68706.2025.11323860
7. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., ... Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
8. Jacobs, M., He, J., Pradier, M. F., Lam, B., Ahn, A. C., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021). Designing AI for trust and collaboration in time-constrained medical decisions. Proceedings of the ACM Conference on Human Factors in Computing Systems.
9. Longoni, C., Bonezzi, A., & Morewedge, C. K. (2021). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650.
10. Kummari, D. N., Burugulla, J. K. R., Malempati, M., Amistapuram, K., Garapati, R. S., & Nagabhyru, K. C. (2025, December). Enhancing Audit Compliance and Operational Efficiency in Manufacturing and Commercial Insurance Through Agentic AI and Data Engineering Frameworks. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 714-720). IEEE.
11. Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2021). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 4(1), 1–10.
12. Topol, E. (2021). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 27(1), 44–56.
13. Yubo, C. (2021). Innovation of enterprise financial management based on machine learning and artificial intelligence technology. Journal of Intelligent and Fuzzy Systems, 40(4), 6767–6778.
14. Balamurugan, J., Bhuvaneswari, M., Aitha, A. R., Nagaraju, S., Viswanathan, R., & Dhasarathan, N. (2025, November). Explanatory Transformer-Based Sequential Recommendation: Assessing BERTRec with SASRec for Customer Behaviour Prediction. In 2025 International Conference on Emerging Engineering Technologies and Applications (IC-EETA) (pp. 470-475). IEEE.
15. Rao, A., Kumar, M. V. M., Sashtry, N. K. B. V., Moonesar, I. A., Ramaprasad, A., & Núñez, A. (2022). Editorial: AI and healthcare financial management toward sustainable development. Frontiers in Artificial Intelligence, 5, 1096496.
16. Radanliev, P., De Roure, D., Walton, R., Van Kleek, M., Santos, O., Ani, U., & Montalvo, R. M. (2022). Artificial intelligence and machine learning in dynamic risk analytics. AI & Society, 37(4), 1441–1456.
17. Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., & Paranjpe, I. (2022). Federated learning for healthcare informatics. NPJ Digital Medicine, 5(1), 1–10.
18. Balamurugan, J., Aitha, A. R., Kishore, D. S. C., Reenaraj, T., Babu, T. S., & Kumar, B. B. (2025, November). Adaptive AI-Driven Optimization in Smart Manufacturing: A Hybrid Neural-Network and Genetic-Algorithm Approach. In 2025 International Conference on Emerging Engineering Technologies and Applications (IC-EETA) (pp. 690-697). IEEE.
19. Lekadir, K., Feragen, A., Fofanah, A. J., Frangi, A. F., Buyx, A., & FUTURE-AI Consortium. (2023). FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ Health & Care Informatics, 30(1).
20. Mesko, B., & Topol, E. (2023). The imperative for trustworthy artificial intelligence in medicine. The Lancet Digital Health, 5(6), e367–e368.
21. Sudhakar, A. V. V., Inala, R., Verma, A. K., Nag, K., Pandey, V., & Anand, P. S. (2025, September). Hybrid Rule-Based and Machine Learning Framework for Embedding Anti-Discrimination Law in Automated Decision Systems. In 2025 International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT) (pp. 1-6). IEEE.
22. Pham, P., Zhang, H., Gao, W., & Zhu, X. (2024). Determinants and performance outcomes of artificial intelligence adoption: Evidence from U.S. hospitals. Journal of Business Research, 172, 114402.
23. Wang, F., Kaushal, R., & Khullar, D. (2024). Should health care demand interpretable artificial intelligence or accept black box medicine? Annals of Internal Medicine, 177(2), 245–252.
24. Bodnari, A., & Travis, J. (2025). Scaling enterprise AI in healthcare: The role of governance in risk mitigation frameworks. NPJ Digital Medicine, 8, 272.
25. Radha, S., Gottimukkala, V. R. R., Thottara, S., & Vandhana, K. (2025, October). Adaptive Video Streaming Over 5G Networks Using Deep Reinforcement Learning with Closed-Loop Feedback Mechanism for Bitrate Control. In 2025 International Conference on Communication, Computer, and Information Technology (IC3IT) (pp. 1-6). IEEE.
26. Angus, D. C., Khera, R., Lieu, T., et al. (2025). AI, health, and health care today and tomorrow: The JAMA Summit report on artificial intelligence. JAMA, 334(18), 1650–1664.
27. Nguyen, T. D., Whaley, C. M., Simon, K., Mehta, N., Yu, H., McBain, R. K., Mehrotra, A., & Cantor, J. H. (2025). Adoption of artificial intelligence in the health care sector. JAMA Health Forum, 6(11), e255029.
28. Inala, R., Kaulwar, P. K., Nagabhyru, K. C., Adusupalli, B., & Arun Raj, S. R. (2025, October). Leveraging IEC 61850 for Interoperable and Resilient Smart Grid Communication Architecture. In International Conference on Microelectronics, Electromagnetics and Telecommunication (pp. 549-566). Cham: Springer Nature Switzerland.
29. Acemoglu, D., Johnson, S., & Kasy, M. (2022). Machine learning and causal inference for policy evaluation and decision-making. National Bureau of Economic Research Working Paper Series.
30. Agarwal, R., Gao, G., DesRoches, C., & Jha, A. K. (2022). Research commentary—The digital transformation of healthcare: Current status and future directions. Information Systems Research, 33(2), 629–645.
31. Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Mohandis, H., & Alkhathami, M. (2023). A review of the role of artificial intelligence in healthcare. Journal of Personalized Medicine, 13(6), 951.
32. Amistapuram, K., Pandiri, L., Raju, V. R., Paleti, S., Singireddy, S., & Sheelam, G. K. (2025, December). AI-Based Cloud Infrastructure and MLOps Frameworks for Scalable Data Engineering Across Banking and Insurance. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 186-192). IEEE.
33. Ali, O., Abdelbaky, M., Shrestha, A., Elbasi, E., Alryalat, M., & Dwivedi, Y. K. (2023). A systematic literature review of artificial intelligence in the finance domain. Internet of Things, 22, 100805.
34. Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., Al Yami, M. S., Al Harbi, S., Albekairy, A. M., & Alshahrani, A. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23, 689.
35. Nigam, N., Sireesha, B., Ediga, P., Segireddy, A. R., & Bokde, S. (2025, December). Comparative Evaluation of Cloud Security Algorithms Using Multiple Classifiers with an Optimized Intrusion Detection System. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-6). IEEE.
36. Ashrafian, H., Darzi, A., & Athanasiou, T. (2021). Artificial intelligence and machine learning in healthcare: Opportunities and challenges. Journal of the Royal Society of Medicine, 114(10), 1–10.
37. Attaran, M. (2022). Digital technology enablers and transformation in business: A review of artificial intelligence, blockchain and cloud computing. Journal of Strategic Innovation and Sustainability, 17(1), 9–28.
38. Oladosu, S. A., Ige, A. B., Ike, C. C., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2023). AI-driven security for next-generation data centers: Conceptualizing autonomous threat detection and response in cloud-connected environments. GSC Adv Res Rev, 15(2), 162-172.
39. BaniHani, I., Alawadi, S., & Elmrayyan, N. (2024). AI and the decision-making process: A literature review in healthcare, financial, and technology sectors. Technology Analysis & Strategic Management.
40. Chatterjee, S., Chaudhuri, R., Vrontis, D., & Thrassou, A. (2023). Assessing the impact of big data analytics on decision-making processes, forecasting, and firm performance. Technological Forecasting and Social Change, 193, 122624.
41. Chen, Y., Argentinis, E., & Weber, G. (2022). Artificial intelligence in healthcare: Past, present, and future. Stroke and Vascular Neurology, 7(3), 230–243.
42. Mangalampalli, B. M., Kolla, S. K., Bandi, V. D. V. K., Yandamuri, U. S., & Rani, P. S. (2025). Designing Intelligent Healthcare Ecosystems through Adaptive Data Integration and Autonomous Learning Systems. Vascular and Endovascular Review, 8(20s), 330-347.
43. Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2022). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 50(1), 24–42.
44. El Naqa, I., Murphy, M. J., & Topol, E. (2023). AI and machine learning in healthcare. Nature Biomedical Engineering, 7(9), 1073–1075.
45. Galetsi, P., Katsaliaki, K., Kumar, S., & Mourtos, I. (2024). The exploitation of data to support decision-making in healthcare: A systematic literature review and future research directions. Management Review Quarterly.
46. Mangala, N., & Kolla, S. H. (2025). Real-Time Feature Engineering for Streaming AI Workloads Using PySpark and Azure Event Hubs. International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management, 1(6), 93-105.
47. Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Suman, R. (2022). Artificial intelligence applications for industry and business transformation: A review. Journal of Industrial Integration and Management, 7(4), 1–29.
48. Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2022). Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55(2), 1–38.
49. Kiseleva, A., Kotzinos, D., & De Hert, P. (2022). Artificial intelligence and healthcare: Ethical and legal perspectives. Computer Law & Security Review, 44, 105689.
50. Gadi, A. L., Garapati, R. S., Inala, R., Singireddy, J., & Kapila, D. (2025, October). Robust Mutual Authentication for Distributed IoT Systems: Balancing Security and Efficiency. In International Conference on Microelectronics, Electromagnetics and Telecommunication (pp. 538-548). Cham: Springer Nature Switzerland.
51. Lekadir, K., Feragen, A., Fofanah, A. J., Frangi, A. F., Buyx, A., & FUTURE-AI Consortium. (2023). FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ Health & Care Informatics, 30(1).
52. Mesko, B., & Topol, E. (2023). The imperative for trustworthy artificial intelligence in medicine. The Lancet Digital Health, 5(6), e367–e368.
53. Rajkumar, S., Secula, F., Cobos, D., et al. (2024). Health information systems data for decision-making: Case study in three cities on current practices and opportunities. Discover Health Systems, 3, 68.
54. Srikanth, T., Segireddy, A. R., & Elavarasi, S. A. (2025, October). STaSFormer-SGAD: Semantic Triplet-Aware Spatial Flow-Guided Spatio-Temporal Graph for Anomaly Detection in Surveillance Videos. In 2025 International Conference on Communication, Computer, and Information Technology (IC3IT) (pp. 1-7). IEEE.
55. Wang, F., Kaushal, R., & Khullar, D. (2024). Should healthcare demand interpretable artificial intelligence or accept black-box medicine? Annals of Internal Medicine, 177(2), 245–252.
56. Abdar, M., Książek, W., Acharya, U. R., Tan, R. S., Makarenkov, V., & Pławiak, P. (2021). A review of uncertainty quantification in deep learning: Techniques, applications, and challenges. Information Fusion, 76, 243–297.
57. Baudier, P., Ammi, C., Deboeuf-Rouchon, M., & Kondrateva, G. (2023). The impact of artificial intelligence on business transformation: A systematic literature review. Technological Forecasting and Social Change, 188, 122295.
58. Bhatt, P., de Pennington, N., Kulendran, M., & Bhangu, A. (2021). Artificial intelligence in healthcare: Current applications and future directions. British Journal of Hospital Medicine, 82(11), 1–9.
59. Bohr, A., & Memarzadeh, K. (2022). Artificial intelligence in healthcare. In Artificial Intelligence in Healthcare (2nd ed., pp. 3–44). Academic Press.
60. Kolla, S. K., & Bandi, V. D. V. K. (2025). Autonomous Clinical Monitoring Platforms Using Reinforcement Learning and Deep Neural Networks. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(6), 13300-13313.
61. Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in organizational decision-making. Business Horizons, 66(5), 641–652.
62. Davenport, T. H., & Mittal, N. (2022). All-in on AI: How smart companies win big with artificial intelligence. Harvard Business Review Press.
63. De Mauro, A., Greco, M., & Grimaldi, M. (2022). What is big data? A consensual definition and a review of key concepts. Information Systems Frontiers, 24(5), 1483–1498.
64. Mattaparthi, R. (2025). GenAI-Augmented Diagnostic Reasoning for Diesel Engine Fault Triage: A Large Language Model Framework for Technician Decision Support at Scale. Journal of Material Sciences & Manufacturing Research, 6(12), 1.
65. Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., Conboy, K., Doyle, R., Dubey, R., Dutot, V., Felix, R., Goyal, D. P., Gustafsson, A., Hinsch, C., Jebabli, I., ... Wamba, S. F. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI. International Journal of Information Management, 71, 102642.
66. Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2022). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 4(11), e745–e750.
67. Pareyani, S., Goswami, S., Geetha, Y., Dimri, S. K., Niharika, D. S., & Amistapuram, K. (2025, December). Smart Resource Allocation in Wireless Sensor Networks Through AI Techniques. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-6). IEEE.
68. Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2022). Medical 4.0 technologies for healthcare: Features, capabilities, and applications. Internet of Things and Cyber-Physical Systems, 2, 12–30.
69. Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes—An interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(1), 5–20.
70. Krittanawong, C., Rogers, A. J., Johnson, K. W., Wang, Z., Turakhia, M. P., Halperin, J. L., & Narayan, S. M. (2021). Integration of novel monitoring devices with machine learning technology for cardiovascular care. Nature Reviews Cardiology, 18(2), 75–91.
71. Radhakrishnan, P., Manoranjithem, V., Garapati, R. S., Singh, S., & Praveen, R. V. S. (2025, November). Random Forest–XGBoost Hybrid Model for Early Detection of Breast Cancer in Medical Imaging Datasets. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1-6). IEEE.
72. Lai, M. C., & Ong, C. S. (2023). Artificial intelligence adoption in financial services: A systematic review. Journal of Financial Services Marketing, 28(3), 356–372.
73. Min, H. (2022). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 25(7), 789–807.
74. Kolla, S. H., & Mangala, N. (2025). DESIGNING AUTONOMOUS LLM AGENT FRAMEWORKS USING GEN AI PIPELINES TO ENHANCE CUSTOMER SERVICE MANAGEMENT AND KNOWLEDGE WORKFLOWS. Lex Localis-Journal of Local Self-Government, 23 (S6), 9719–9733.
75. Ngiam, K. Y., & Khor, I. W. (2021). Big data and machine learning algorithms for healthcare delivery. The Lancet Oncology, 22(6), e262–e273.
76. Nishant, R., Kennedy, M., & Corbett, J. (2021). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104.
77. Mangala, N. (2025). Agentic Data Pipelines: Autonomous ELT Orchestration Using AI Agents on Microsoft Fabric and Databricks. International Journal of Computer Technology and Electronics Communication, 8(6), 11891-11907.
78. Ransbotham, S., Khodabandeh, S., Kiron, D., Candelon, F., Chu, M., & LaFountain, B. (2022). Expanding AI's impact with organizational learning. MIT Sloan Management Review, 63(4), 1–27.
79. Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2021). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 63(3), 66–83.
80. Wamba, S. F., Bawack, R. E., Guthrie, C., Queiroz, M. M., & Carillo, K. D. A. (2021). Are we preparing for a good AI society? A bibliometric review and research agenda. Technological Forecasting and Social Change, 164, 120482.
81.
82. Mangalampalli, B. M., & Kolla, S. K. (2025). Large Language Models for Automated Healthcare Data Dictionary Generation and Maintenance. Vascular and Endovascular Review, 8(20s), 363-375.
83. Xu, Z., Li, J., Wang, H., & Chen, Y. (2025). Explainable artificial intelligence for intelligent decision support systems: A comprehensive review. Expert Systems with Applications, 257, 124985.
84. Ahmad, O., Hall, M. A., & Majeed, A. (2021). Artificial intelligence and machine learning in clinical medicine, 2021. Clinical Medicine, 21(3), e314–e318.
85. Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2022). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 22, 310.
86. Lebcir, I., Mageswari, S. U., Bhosale, Y. H., Nagubandi, A. R., & Mahabooba, M. M. Agile Strategic Management in the Age of Disruption: Leveraging AI and Data Analytics for Competitive Advantage.
87. Arora, S., & Chakraborty, C. (2023). Artificial intelligence in financial technology: Applications, challenges, and future opportunities. Financial Innovation, 9(1), 84.
88. Bertsimas, D., Dunn, J., Pawlowski, C., Silberholz, J., & Zhuo, Y. D. (2022). Applied machine learning for healthcare operations and decision support. INFORMS Journal on Applied Analytics, 52(5), 357–374.
89. Kolla, S. H. (2025). Autonomous Agentic Frameworks for Enterprise Service Operations and Intelligent Process Automation. International Journal of Science, Research and Technology, 8(4), 14643-14655.
90. Choi, T. M., Wallace, S. W., & Wang, Y. (2022). Big data analytics in operations management. Production and Operations Management, 31(1), 1–17.
91. Davenport, T. H., & Bean, R. (2023). Generative AI and the future of business decision-making. MIT Sloan Management Review, 65(1), 22–30.
92. De Mauro, A., Greco, M., & Grimaldi, M. (2023). Artificial intelligence adoption in business organizations: Trends and future research directions. Information Systems Frontiers, 25(6), 2127–2144.
93. Haleem, A., Javaid, M., Singh, R. P., Suman, R., & Rab, S. (2023). Artificial intelligence applications for improving healthcare systems: A systematic review. Sensors, 23(8), 3916.
94. Kolla, T. (2025). Generative AI for Intelligent Medical Coding and Healthcare Analytics. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(6), 13285-13299.
95. Huang, M. H., & Rust, R. T. (2022). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 50(1), 30–50.
96. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2022). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 7(3), 230–243.
97. Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: Four decades of research. Management Review Quarterly, 71(1), 91–134.
98. Kolla, T. (2025). Anomaly Detection Models for Outlier Provider Behavior in Cost and Treatment Patterns. Journal of Computational Analysis & Applications, 34(12), 1204.
99. Kwon, O., Sim, J. M., & Kim, K. (2023). Data-driven decision-making using artificial intelligence: A systematic literature review. Decision Support Systems, 168, 113934.
100. Min, H. (2023). Artificial intelligence, analytics, and digital transformation for business resilience. Journal of Business Research, 162, 113873.
101. Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011-1027.
102. Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2024). Artificial intelligence adoption in organizations: A review and future research agenda. International Journal of Information Management, 76, 102746.
103. Reddy, S. (2022). Explainable artificial intelligence for clinical and healthcare applications. The Lancet Digital Health, 4(4), e214–e215.
104. Reddy, V. A. R. (2025). Journal of Rare Cardiovascular Diseases. Health, 5(3), 402-422.
105. Shrestha, Y. R., Krishna, V., & von Krogh, G. (2023). Augmenting organizational decision-making with artificial intelligence. California Management Review, 65(3), 43–69.
106. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
107. Sun, T. Q., & Medaglia, R. (2022). Mapping the challenges of artificial intelligence in the public sector. Government Information Quarterly, 39(2), 101657.
108. Topol, E. J. (2024). Artificial intelligence and the future of medicine: From prediction to precision care. Nature Medicine, 30(2), 250–258.
109. Wamba, S. F., Queiroz, M. M., Trinchera, L., & Guthrie, C. (2024). Artificial intelligence capability and organizational performance: Evidence from digital transformation initiatives. Technological Forecasting and Social Change, 199, 123455.
110. Nagubandi, A. R. (2025). FinTech and Financial Inclusion in Emerging Economies: An Empirical Assessment. Advances in Consumer Research.
111. Wilson, H. J., Daugherty, P. R., & Bianzino, N. M. (2023). The collaborative future of artificial intelligence and human decision-making. Harvard Business Review, 101(4), 64–73.
112. Bandi, V. D. V. K. AI-Based Anomaly Detection Frameworks in Distributed Enterprise Data Systems.
Additional Files
Published
Issue
Section
License
Copyright (c) 2025 Alejandro Fernández (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The American Online Journal of Science and Engineering (AOJSE) is an open-access journal, and all published articles are made freely available to readers worldwide under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license governs the use, sharing, adaptation, and distribution of all content published in AOJSE and is designed to promote the broadest possible dissemination and reuse of scholarly research.
Creative Commons Attribution 4.0 International License (CC BY 4.0)
Under the Creative Commons Attribution 4.0 International License, any user is free to share, copy, and redistribute the published material in any medium or format, and to adapt, remix, transform, and build upon the material for any purpose, including commercial use, provided that appropriate credit is given to the original authors and source. The license cannot be revoked as long as the terms are followed.
When using or redistributing content published in AOJSE, users must provide proper attribution by citing the original authors' names, the article title, the journal name (AOJSE), the volume and issue number, the year of publication, and the DOI or URL of the article. Users must also indicate if any changes were made to the original work. Attribution must be provided in a reasonable manner but must not suggest that the authors or AOJSE endorse the user or their use of the material.
Author Rights and Copyright Retention
AOJSE respects and upholds the intellectual property rights of all contributing authors. Authors who publish in AOJSE retain full copyright over their work. By submitting a manuscript to AOJSE, authors grant the journal a non-exclusive, worldwide, royalty-free license to publish, reproduce, distribute, publicly display, and archive the article in print and electronic formats. This license allows AOJSE to make the article freely accessible to readers globally while ensuring that the authors remain the rightful owners of their work.
Authors are permitted to deposit their published articles in institutional repositories, personal websites, academic networking platforms, and preprint servers, provided that the original publication in AOJSE is acknowledged and properly cited. Authors may also reuse their published content in subsequent works, presentations, teaching materials, and grant applications without requiring prior permission from the journal.
Third-Party Content
Authors are solely responsible for obtaining written permission to reproduce any third-party material, including figures, tables, images, data, or excerpts from other publications, included in their manuscript. Evidence of such permissions must be provided to the editorial office upon request. AOJSE does not assume any responsibility for copyright infringement arising from the unauthorized inclusion of third-party content in published articles.
Permitted Uses
Under the CC BY 4.0 license, the following uses of AOJSE published content are freely permitted without prior written permission from the journal or authors, provided that proper attribution is given. Users may read, download, print, and distribute articles for personal, educational, or research purposes. Researchers may reuse data, figures, and findings for meta-analyses, systematic reviews, and secondary research. Educators may incorporate published articles into course materials, syllabi, and academic presentations. Journalists, policymakers, and practitioners may reference and cite published findings for professional and public interest purposes.
Prohibited Uses
While the CC BY 4.0 license is broad and permissive, users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Users may not misrepresent the original authorship of published content or imply endorsement by the original authors or AOJSE without explicit consent. Plagiarism, data fabrication, and any form of academic misconduct in the reuse of published material are strictly prohibited and are a violation of publication ethics standards.
Institutional and Repository Deposit Policy
AOJSE fully supports self-archiving and green open access. Authors are encouraged to deposit the final published version of their article, also known as the version of record, in institutional repositories, subject repositories, and open-access databases immediately upon publication. There is no embargo period. Authors should always link back to the original article on the AOJSE website and include the full citation and DOI when depositing their work in any repository.
Digital Preservation and Archiving
AOJSE is committed to the long-term digital preservation of all published content to ensure its permanent availability and accessibility. The journal utilizes the Open Journal Systems (OJS) platform, which supports internationally recognized digital archiving standards. AOJSE encourages participation in archiving networks such as LOCKSS (Lots of Copies Keep Stuff Safe) and CLOCKSS (Controlled LOCKSS) to safeguard published articles against data loss and ensure continued access for future generations of researchers and readers.
Disclaimer
The views, opinions, findings, and conclusions expressed in articles published in AOJSE are solely those of the authors and do not necessarily reflect the official position, policy, or views of the journal, its editorial board, or its affiliated organizations. AOJSE makes every effort to ensure the accuracy and integrity of published content but does not accept legal responsibility for any errors, omissions, or claims arising from the use of information published in the journal.
Contact for Licensing Inquiries
For any questions regarding the licensing terms, copyright, or permitted use of content published in AOJSE, please contact the editorial office through the journal website at https://aojse.org. The editorial team will be happy to assist authors, readers, and institutions with any licensing-related queries.
