Artificial Intelligence in the Internet of Medical Things (IoMT) for Holistic Diabetic Care: Outcomes, Architecture, and Industrial Impact

Abstract

Diabetes management experiences significant advancement as Artificial Intelligence combines with Internet of Medical Things platforms, creating continuous surveillance systems. Individuals gain access to forecasting tools and automated control of metabolic parameters. These systems influence several organ networks, including pancreatic, hepatic, renal, and peripheral components, producing improved glucose regulation and timely detection of emerging complications. Automated processes refine insulin delivery, achieving greater precision than conventional manual adjustment protocols. Patients exhibit improved outcomes through these technologies, demonstrating fewer acute events and maintaining glucose stability throughout daily periods. Food and pharmaceutical industries derive measurable advantages as patient data shapes formulation decisions and operational strategies. Nutrition labeling evolves from standardized indices toward individualized response metrics reflecting personal glycemic patterns. Cloud computing infrastructure processes continuous device data while maintaining confidentiality requirements and regulatory compliance. Food manufacturers adjust products based on observed glucose responses, whereas pharmaceutical operations modify production processes and distribution systems according to utilization patterns. Conventional scheduled clinical encounters transition toward continuous personalized monitoring, accommodating individual metabolic profiles and behavioral patterns. Clinical evidence generated through these systems influences product development across sectors, establishing data-driven connections between patient outcomes and manufacturing decisions. The integrated ecosystem positions real-world effectiveness data as foundational input for therapeutic optimization and industry innovation.