Autonomous Preventive Healthcare Systems Combining IoT Sensors, AI, and Behavioral Nudging

Internet of Things (IoT) sensor networks, combined with Artificial Intelligence (AI) enabled analytics and behavioral nudging paradigms, have ushered in a paradigm shift in the field of autonomous preventive health care. The marriage of Internet of Things (IoT) sensor networks, Artificial Intelligence (AI) analytics, and behavioral nudging paradigms has emerged as a paradigm shift in the realm of autonomous preventive health care. The functional integration of wearable and implantable biosensors, machine learning (ML) algorithms, and nudge-based digital behavior change interventions for proactive, personalized health management before the development of clinical disease is reviewed. The paper is based upon 17 peer reviewed references published between 2018 and 2021, which define architectural principles, performance requirements, and constraints of such integrated systems. Quantitative analyses show that hybrid AI models (CNN with LSTM) can reach a maximum diagnostic accuracy of 96.1%, while support vector machines with the same IoT data streams can reach 87.2%. The highest observed adherence gain (22.1%) and 90-day retention rate (69%) were achieved with behavioral nudging via restructuring default options, among the modalities assessed. The integrated platform provides a 76% latency reduction in preventive interventions over architectures involving the Internet of Things alone, and a USD 1,870 annual patient cost savings after deployment, compared to traditional care pathways, which represents a 34.2% decrease. In addition to regulatory dimensions, security vulnerabilities and limitations of interoperability are examined, and ethical considerations of algorithmic decision making are addressed. The paper's authors conclude that achieving autonomous preventive healthcare requires a synergy of sensor accuracy, adaptive AI, and human behavior science within a privacy-enhancing and scalable framework.