International Journal of Science, Engineering and TechnologyAuthors: Kairav Desai
Abstract: The transition from traditional healthcare records to intelligent Health Information Systems (IHIS) represents a fundamental shift in medical engineering, driven by the convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Cloud Computing. This review article provides a comprehensive analysis of the design principles and architectural methodologies required to build autonomous, end-to-end medical ecosystems. We explore the system engineering lifecycle, beginning with the design of high-fidelity, energy-aware Internet of Medical Things (IoMT) sensing layers that ensure continuous, non-intrusive data acquisition. Central to the discussion is the architectural transition toward a microservices-based, cloud-native infrastructure that leverages the edge-fog-cloud continuum to satisfy both real-time latency requirements and long-term big data analytics. The processing layer is analyzed through the lens of clinical-grade AI design, emphasizing the importance of algorithm selection, explainability (XAI), and validation against medical regulatory standards. Furthermore, we address critical design challenges, including semantic interoperability via HL7 FHIR standards, security-by-design through blockchain and federated learning, and the ethical mitigation of algorithmic bias. By synthesizing recent case studies in intelligent intensive care and telemedicine, this review identifies future research frontiers such as 6G-enabled tactile internet and personalized digital health twins. The findings offer a roadmap for engineers and clinicians to design resilient, interoperable, and human-centric systems that transform raw biometric data into life-saving clinical intelligence.
DOI: https://doi.org/10.5281/zenodo.18221694
