Next-Generation Intelligent Systems in Modern Governance: Applications in Healthcare, Smart Mobility, Defense, and Economic Systems
DOI:
https://doi.org/10.61424/jcsit.v3i2.1048Keywords:
Artificial Intelligence, Intelligent Governance, Healthcare AI, Smart Mobility, Risk ManagementAbstract
Next-generation intelligent systems are changing the face of governance by combining artificial intelligence, distributed computing, real-time analytics, and adaptive decision-making in healthcare, smart mobility, defence, and economic systems. The study explores key competencies, governance mechanisms, areas of application, deployment practices, performance assessment methods, ethical issues, and future trends of these systems. Specific focus is on lifecycle-based governance, starting with data acquisition, model development, validation, deployment, continuous monitoring, risk assessment, modification, and decommissioning. The core technologies that are believed to enable low-latency, scalable, and context-aware governance are edge intelligence, decentralized computing, knowledge-based technologies, intelligent networking, and high-performance infrastructure. The analysis emphasizes the value of oversight by humans, transparency, accountability, protection of privacy, fair treatment, documentation, local validation, auditing, and compliance with regulations in the context of high-impact applications. Clinical safety, risk classification, post-deployment monitoring, and human override are highlighted by healthcare examples; smart mobility applications showcase the benefits of connected vehicles, IoT sensing, real-time analytics, and adaptive transportation management. Performance evaluation criteria include reliability, latency, fairness, robustness, drift, policy compliance, security, and user-reported incidents, and should be multidimensional. Some challenges that have not been resolved are the lack of transparency around algorithms, the varying quality of the data, the diversity of evaluation metrics, the varying regulations, complex AI ecosystems, and fragmented governance. Standardization of testing, ongoing assurance, stakeholder engagement, adaptive regulation, and international harmonization should continue to be a focus of future governance.
References
Abdullah C. A. and Ridoy M. J. I., (2023) Artificial intelligence-driven cyber threat detection for protecting critical digital infrastructure, Innov.: Int. Multidiscipl. J. Appl. Technol., vol. 1, no. 2, pp. 76–83, 2023, doi: 10.51699/zb8kwv06.
Abdullah C. A. and Ridoy M. J. I., (2025) Artificial intelligence driven infrastructure security enhancing cybersecurity and protecting national security systems, J. Technol. Sci., vol. 1, no. 3, pp. 191–200, 2025, doi: 10.61796/ipteks.v1i3.498.
Al-Sanjary O. I., Vasuthevan S., Omer H. K., Mohammed M. N., and Abdullah M. I., (2019) An Intelligent Recycling Bin Using Wireless Sensor Network Technology, in Proc. 2019 IEEE Int. Conf. Autom. Control Intell. Syst. (I2CACIS), Selangor, Malaysia, 2019, pp. 30–33, doi: 10.1109/I2CACIS.2019.8825044.
Baker S. B., Xiang W., and Atkinson I., (2017) Internet of things for smart healthcare: Technologies, challenges, and opportunities, IEEE Access, vol. 5, pp. 26521–26544, 2017, doi: 10.1109/ACCESS.2017.2775180.
Balasundaram A., Routray S., Prabu A., Krishnan P., Malla P. P., and Maiti M., (2023) Internet of things (IoT)-based smart healthcare system for efficient diagnostics of health parameters of patients in emergency care, IEEE Internet Things J., vol. 10, pp. 18563–18570, 2023, doi: 10.1109/JIOT.2023.3246065.
Cai Q., Wang H., Li Z., and Liu X., (2019) A survey on multimodal data-driven smart healthcare systems: Approaches and applications, IEEE Access, vol. 7, pp. 133583–133599, 2019, doi: 10.1109/ACCESS.2019.2941419.
Cui Q., Ding Z., and Chen F., (2024) Hybrid Directed Hypergraph Learning and Forecasting of Skeleton-Based Human Poses, Cyborg Bionic Syst., vol. 5, Art. no. 0093, 2024, doi: 10.34133/cbsystems.0093.
Du M., Li Z., Bian L., Randriamahazaka H., and Chen W., (2025) Two-dimensional materials van der Waals assembly enabling scalable smart textiles, Mater. Sci. Eng. R Rep., vol. 163, Art. no. 100915, 2025, doi: 10.1016/j.mser.2024.100915.
Gardašević G., Katzis K., Bajić D., and Berbakov L., (2020) Emerging wireless sensor networks and Internet of Things technologies—Foundations of smart healthcare, Sensors, vol. 20, Art. no. 3619, 2020, doi: 10.3390/s20133619.
He W., Zhu J., Feng Y., Liang F., You K., Chai H., and Wang W., (2024) Neuromorphic-enabled video-activated cell sorting, Nat. Commun., vol. 15, Art. no. 10792, 2024, doi: 10.1038/s41467-024-55094-0.
Hossain M. K. and Thakur V., (2024) A performance management framework for smart health-care supply chain based on Industry 4.0 technologies, J. Glob. Oper. Strateg. Sourc., vol. 18, pp. 285–306, 2024, doi: 10.1108/JGOSS-12-2022-0123.
Hu F., Yang H., Qiu L., Wei S., Hu H., and Zhou H., (2025) Spatial structure and organization of the medical device industry urban network in China: Evidence from Specialized, Refined, Distinctive, and Innovative firms, Front. Public Health, vol. 13, Art. no. 1518327, 2025, doi: 10.3389/fpubh.2025.1518327.
Ibrahim Y., Abdel-Malek M. A., Azab M., and Rizk M. R., (2025) Privacy-preserved mutually-trusted 5G communications in presence of pervasive attacks, Internet Things, vol. 30, Art. no. 101491, 2025, doi: 10.1016/j.iot.2025.101491.
Ijaz M., Li G., Wang H., El-Sherbeeny A. M., Moro A Y., Lin L., Koubaa A., and Noor A., (2015) Intelligent fog-enabled smart healthcare system for wearable physiological parameter detection, Electronics, vol. 9, Art. no. 2015, 2020, doi: 10.3390/electronics9122015.
Istepanian R. S. and Al-Anzi T., (2018) m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics, Methods, vol. 151, pp. 34–40, 2018, doi: 10.1016/j.ymeth.2018.05.015.
Kandeel M., (2024) Revolutionizing Healthcare: Harnessing the Power of Artificial Intelligence for Enhanced Diagnostics, Treatment and Drug Discovery, Int. J. Pharmacol., vol. 20, pp. 1–10, 2024, doi: 10.3923/ijp.2024.1.10.
Khalid U., Chen L., Khan A. A., Chen B., Mehmood F., and Yasir M., (2025) A smart facial acne disease monitoring for automate severity assessment using AI-enabled cloud-based internet of things, Discov. Comput., vol. 28, Art. no. 12, 2025, doi: 10.1007/s10791-025-09503-7.
Khan M. and Hossni Y., (2025) A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition, Sci. Rep., vol. 15, Art. no. 3858, 2025, doi: 10.1038/s41598-025-88378-6.
Kumar A., Gupta R., Kumar S., Dutta K., and Rani M., (2025) Securing IoMT-based healthcare system: Issues, challenges, and solutions, in Artificial Intelligence and Cybersecurity in Healthcare Cyber Physical Systems, R. Agrawal, P. S. Rathore, G. G. Devarajan, and R. R. Divivedi, Eds. Beverly, MA, USA: Scrivener Publishing LLC, 2025, pp. 17–56, doi: 10.1002/9781394229826.ch2.
Lakshmi S. G., Lalitha C. N., Lavanya S., Vijaya L. S., and Sri-Lakshmi P. S., (2025) IOT-enabled cloud solutions for reliable health monitoring, in Emerging Trends in Computer Science and Its Application, Boca Raton, FL, USA: CRC Press, 2025, pp. 278–282, doi: 10.1201/9781003606635-45.
Li W., Chai Y., Khan F., Jan S. R. U., Verma S., Menon V. G., Kavita F., and Li X., (2021) A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system, Mob. Netw. Appl., vol. 26, pp. 234–252, 2021, doi: 10.1007/s11036-020-01700-6.
Magara T. and Zhou Y., (2024) EMAKAS: An efficient three-factor mutual authentication and key-agreement scheme for IoT environment, Cyber. Secur. Appl., vol. 3, Art. no. 100066, 2025, doi: 10.1016/j.csa.2024.100066.
Nguyen D. C., Pham Q.-V., Pathirana P. N., Ding M., Seneviratne A., Lin Z., Dobre O., and Hwang W.-J., (2022) Federated learning for smart healthcare: A survey, ACM Comput. Surv., vol. 55, pp. 1–37, 2022, doi: 10.1145/3501296.
Pham T. P. T. and Huang M. C., (2025) Exploring the relationship between nursing professional values and job satisfaction in Vietnam, Nurs. Pract. Today, vol. 12, no. 1, pp. 55–64, 2025, doi: 10.18502/npt.v12i1.17525.
Qi K., (2025) Advancing hospital healthcare: Achieving IoT-based secure health monitoring through multilayer machine learning, J. Big Data, vol. 12, Art. no. 1, 2025, doi: 10.1186/s40537-024-01038-w.
Rahman A., Debnath T., Kundu D., Khan M. S. I., Aishi A. A., Sazzad S., Sayduzzaman M., and Band S. S., (2024) Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities, AIMS Public Health, vol. 11, Art. no. 58, 2024, doi: 10.3934/publichealth.2024004.
Ridoy M. J. I. and Abdullah C. A., (2021) AI-driven threat intelligence and cybersecurity performance in US organizations: Examining the roles of adoption, system complexity, and workforce expertise, Bus. Soc. Sci., vol. 1, no. 1, 1–8, 2021, doi: 10.25163/business.1110777.
Ridoy M. J. I. and Akhir A., (2025) Smart transportation solutions using AI to optimize traffic flow and reduce urban congestion for improved urban mobility, Int. J. Orange Technol., vol. 7, no. 2, pp. 116–123, 2025, doi: 10.31149/ijot.v7i2.5707.
Ridoy M. J. I. and Islam A., (2025) Integrating machine learning into financial systems to improve risk management and economic stability, J. Knowl. Learn. Sci. Technol., vol. 4, no. 4, pp. 119–126, 2025, doi: 10.60087/jklst.vol4.n4.013.
Ridoy M. J. I., (2022) Predicting supply chain resilience with machine learning: A comparative analysis of ensemble and neural approaches across logistics, manufacturing, healthcare, and agriculture, J. Primeasia, vol. 3, no. 1, pp. 1–8, 2022, doi: 10.25163/primeasia.3110776.
Salman M., Munawar H. S., Latif K., Akram M. W., Khan S. I., and Ullah F., (2022) Big data management in drug-drug interaction: A modern deep learning approach for smart healthcare, Big Data Cogn. Comput., vol. 6, Art. no. 30, 2022, doi: 10.3390/bdcc6010030.
Selem M., Jemili F., and Korbaa O., (2025) Deep learning for intrusion detection in IoT networks, Peer-Peer Netw. Appl., vol. 18, Art. no. 22, 2025, doi: 10.1007/s12083-024-01819-3.
Selna A., Othman Z., Tham J., and Yoosuf A. K., (2022) Challenges to using electronic health records to enhance patient safety, in a Small Island Developing State (SIDS) context, Rec. Manag. J., vol. 32, pp. 249–259, 2022, doi: 10.1108/RMJ-03-2022-0008.
Shama A. T. and Biswas A., (2023) Cloud Misconfiguration as a Governance Failure in AI-Enabled Healthcare and Finance with Privacy Risk Implications, International Journal on Economics, Finance and Sustainable Development, vol. 5, no. 1, pp. 208–214, 2023. doi: 10.31149/ijefsd.v5i1.5799.
Shama A. T., (2024) Third-Party Risk Management in API-Driven Ecosystems: Continuous Vendor Security Practices and Emerging Challenges, American Journal of Technology Advancement, vol. 1, no. 4, pp. 42–50, 2024. doi: 10.31149/ajta.v1i4.4322.
Shama A. T., (2025) AI Governance Challenges in Banking: Model Explainability, Regulatory Compliance, and the Limits of Traditional Risk Management, Business and Social Sciences, vol. 3, no. 1, pp. 1–10, 2025. doi: 10.25163/business.3110901.
Shama A. T., (2025) AI Governance Challenges in Banking: Model Explainability, Regulatory Compliance, and the Limits of Traditional Risk Management, Business and Social Sciences, vol. 3, no. 1, pp. 1–10, 2025. doi: 10.25163/business.3110901.
Sikdar S. and Guha S., (2020) Advancements of healthcare technologies: Paradigm towards smart healthcare systems, in Recent Trends Image Signal Process. Comput. Vis., vol. 1124, pp. 113–132, 2020, doi: 10.1007/978-981-15-2740-1_9.
Taha K., (2025) Big Data Analytics in IoT, social media, NLP, and information security: Trends, challenges, and applications, J. Big Data, vol. 12, Art. no. 150, 2025, doi: 10.1186/s40537-025-01192-9.
Taware R. D., Deshmukh A., Singh C., and Rathod N., (2025) Magnitude of data science & big data in fitness care, AIP Conf. Proc., vol. 3162, Art. no. 020020, 2025, doi: 10.1063/5.0243796.
Wang W., Bo X., Li W., Eldaly A. B. M., Wang L., Li W. J., Chan L. L. H., and Daoud W. A., (2025) Triboelectric Bending Sensors for AI-Enabled Sign Language Recognition, Adv. Sci., vol. 12, Art. no. 2408384, 2025, doi: 10.1002/advs.202408384.
Xiao X., Li Y., Wu Q., Liu X., Cao X., Li M., and Dai X., (2025) Development and validation of a novel predictive model for dementia risk in middle-aged and elderly depression individuals: A large and longitudinal machine learning cohort study, Alzheimer's Res. Ther., vol. 17, Art. no. 103, 2025, doi: 10.1186/s13195-025-01750-6.
Xie Q. and Ding Z., (2025) Provably secure and lightweight blockchain based cross hospital authentication scheme for IoMT-based healthcare, Sci. Rep., vol. 15, Art. no. 6461, 2025, doi: 10.1038/s41598-025-90219-5.
Zhang G., Song C., Yin M., Liu L., Zhang Y., Li Y., and Li C., (2025) TRAPT: A multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data, Nat. Commun., vol. 16, Art. no. 3611, 2025, doi: 10.1038/s41467-025-58921-0.
Zhao X. and Ge B., (2010) An indicator framework for assessing the readiness of hospital in the smart healthcare transformation, IOP Conf. Ser. Earth Environ. Sci., vol. 1101, Art. no. 072010, 2022, doi: 10.1088/1755-1315/1101/7/072010.
Zhou Z., Jin Y., Fu J., Si S., Liu M., Hu Y., Gan J., Deng Y., Li R., and Yang J., (2025) Smart wireless flexible sensing system for unconstrained monitoring of ballistocardiogram and respiration, npj Flex. Electron., vol. 9, Art. no. 15, 2025, doi: 10.1038/s41528-025-00388-6.
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