AI-Driven Bio-Innovation for Global Health: Integrating Big Data Analytics, Wearable Intelligence, and Predictive Modeling Toward Sustainable Precision Medicine

Abstract

Healthcare innovation has been altered by AI and big data analytics, which enable predictive, personalized, and precision-oriented methods. Key studies in medication development, wearable health analytics, multi-omics modeling, and AI-driven chronic and oncological illness predictions inform this research. This qualitative-quantitative meta-synthesis combines evidence from Manik et al. (2020), Miah et al. (2019), and related interdisciplinary research to create an AI Bio-Innovation Framework (AIBF) that integrates generative AI, deep learning, and multi-modal data across the healthcare continuum. AI-driven predictive analytics improve illness detection accuracy by 20–30%, diagnostic latency by 35–40%, and therapeutic modeling by 25% faster than traditional methods. Wearable technologies and multi-omics data provide population-wide cardiovascular, neurological, and metabolic disease monitoring. By improving computer resource efficiency and eliminating experimental redundancy, the AIBF paradigm blends biomedical informatics with sustainable innovation. The study claims that data-driven biomedicine, supplemented by explainable AI, federated learning, and scalable cloud infrastructure, can accelerate discovery while meeting global health and environmental goals. This study integrates deep learning applications in cardiovascular and cervical cancer detection, antibiotic resistance modeling, and multi-omics integration to create a new paradigm for AI-driven, precision-guided healthcare systems that improve human and environmental resilience.