Artificial Intelligence at the Intersection of Cloud Technologies Enterprise Process Automation and Digital Healthcare Innovation
Keywords:
Artificial Intelligence, Cloud Computing, Process Automation, Digital Healthcare, Predictive Analytics, Interoperability, Healthcare IT, Smart SystemsAbstract
This paper investigates the synergetic role of Artificial Intelligence (AI) at the crossroads of cloud technologies, enterprise process automation (EPA), and digital healthcare innovation. The transformative capability of AI to analyze, predict, and automate has significantly impacted operational efficiency, real-time decision-making, and patient care optimization. By embedding AI into cloud infrastructures and enterprise systems, healthcare organizations can automate diagnostics, personalize treatments, and scale services. We review recent literature and highlight key applications, challenges, and future pathways. The paper concludes with a framework for integrating these domains toward resilient, intelligent healthcare systems.
References
Topol, Eric. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019.
Subramanyam, S.V. (2019). The role of artificial intelligence in revolutionizing healthcare business process automation. International Journal of Computer Engineering and Technology (IJCET), 10(4), 88–103.
Jiang, Fei, et al. “Artificial Intelligence in Healthcare: Past, Present and Future.” Stroke and Vascular Neurology, vol. 2, no. 4, 2017, pp. 230–243.
Razzak, Muhammad Imran, et al. “Big Data Analytics for Preventive Medicine.” Neural Computing and Applications, vol. 32, 2019, pp. 4417–4451.
Esteva, Andre, et al. “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks.” Nature, vol. 542, no. 7639, 2017, pp. 115–118.
Subramanyam, S.V. (2022). AI-powered process automation: Unlocking cost efficiency and operational excellence in healthcare systems. International Journal of Advanced Research in Engineering and Technology (IJARET), 13(1), 86–102.
HIMSS Analytics. AI and RPA Adoption in US Hospitals: 2020 Survey Report. Healthcare Information and Management Systems Society, 2020.
Rajkomar, Alvin, et al. “Scalable and Accurate Deep Learning with Electronic Health Records.” NPJ Digital Medicine, vol. 1, 2018, article 18.
Mukesh, V. (2024). A Comprehensive Review of Advanced Machine Learning Techniques for Enhancing Cybersecurity in Blockchain Networks. ISCSITR-International Journal of Artificial Intelligence, 5(1), 1–6.
Sankar Narayanan .S System Analyst, Anna University Coimbatore , 2010. PATTERN BASED SOFTWARE PATENT.International Journal of Computer Engineering and Technology (IJCET) -Volume:1,Issue:1,Pages:8-17.
Subramanyam, S.V. (2024). Transforming financial systems through robotic process automation and AI: The future of smart finance. International Journal of Artificial Intelligence Research and Development (IJAIRD), 2(1), 203–223.
Shickel, Benjamin, et al. “Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis.” Journal of Biomedical Informatics, vol. 83, 2018, pp. 168–185.
Mukesh, V. (2022). Cloud Computing Cybersecurity Enhanced by Machine Learning Techniques. Frontiers in Computer Science and Information Technology (FCSIT), 3(1), 1-19.
Sankar Narayanan .S System Analyst, Anna University Coimbatore , 2010. PATTERN BASED SOFTWARE PATENT.International Journal of Computer Engineering and Technology (IJCET) -Volume:1,Issue:1,Pages:8-17.
Nguyen, Thang, et al. “Artificial Intelligence in Telemedicine: A Systematic Review.” Computer Methods and Programs in Biomedicine, vol. 200, 2021, article 105900.
Miotto, Riccardo, et al. “Deep Learning for Healthcare: Review, Opportunities and Challenges.” Briefings in Bioinformatics, vol. 19, no. 6, 2017, pp. 1236–1246.
Subramanyam, S.V. (2023). The intersection of cloud, AI, and IoT: A pre-2021 framework for healthcare business process transformation. International Journal of Cloud Computing (IJCC), 1(1), 53–69.
Mukesh, V., Joel, D., Balaji, V. M., Tamilpriyan, R., & Yogesh Pandian, S. (2024). Data management and creation of routes for automated vehicles in smart city. International Journal of Computer Engineering and Technology (IJCET), 15(36), 2119–2150. doi: https://doi.org/10.5281/zenodo.14993009
Sharma, Ashish, et al. “Using Digital Health Technology to Better Generate Evidence and Deliver Evidence-Based Care.” Journal of the American College of Cardiology, vol. 71, no. 23, 2018, pp. 2680–2690.
Davenport, Thomas, and Rajeev Kalakota. “The Potential for Artificial Intelligence in Healthcare.” Future Healthcare Journal, vol. 6, no. 2, 2019, pp. 94–98.
Sankar Narayanan .S, System Analyst, Anna University Coimbatore , 2010. INTELLECTUAL PROPERY RIGHTS: ECONOMY Vs SCIENCE &TECHNOLOGY. International Journal of Intellectual Property Rights (IJIPR) .Volume:1,Issue:1,Pages:6-10.
Morley, Jessica, et al. “The Ethics of AI in Health Care: A Mapping Review.” Social Science & Medicine, vol. 260, 2020, article 113172.
Ransbotham, Sam, et al. “Artificial Intelligence in Business Gets Real.” MIT Sloan Management Review, vol. 60, no. 1, 2018, pp. 1–11.
Subramanyam, S.V. (2021). Cloud computing and business process re-engineering in financial systems: The future of digital transformation. International Journal of Information Technology and Management Information Systems (IJITMIS), 12(1), 126–143.
Islam, S. M. Riazul, et al. “The Internet of Things for Health Care: A Comprehensive Survey.” IEEE Access, vol. 3, 2015, pp. 678–708.
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