AI-Driven Biomedical Innovation: Integrating Big Data Analytics, Wearable Intelligence, and Predictive Modeling for Global Health and Sustainability

Abstract

Artificial intelligence (AI) and big data analytics have transformed biomedical innovation by facilitating predictive, personalized, and precision-oriented methodologies in healthcare. This research integrates the methodological and empirical contributions of key studies in drug development, wearable health analytics, multi-omics modeling, and AI-driven predictions of chronic and oncological diseases. This study employs a qualitative-quantitative meta-synthesis to amalgamate evidence from Manik et al. (2020), Miah et al. (2019), and related interdisciplinary research to develop an AI Bio-Innovation Framework (AIBF) that integrates generative AI, deep learning, and multi-modal data across the healthcare continuum. Research indicates that AI-driven predictive analytics enhance disease detection accuracy by 20–30%, decrease diagnostic latency by 35–40%, and facilitate 25% quicker therapy modeling relative to traditional methods. Furthermore, the integration of wearable technology and multi-omics data facilitates real-time, population-wide monitoring of cardiovascular, neurological, and metabolic diseases. The AIBF model integrates biomedical informatics with sustainable innovation by enhancing computing resource efficiency and reducing experimental redundancy. The research asserts that data-driven biomedicine, enhanced by explainable AI, federated learning, and scalable cloud infrastructure, can expedite discovery processes while adhering to global health and environmental goals. This work synthesizes deep learning applications in cardiovascular and cervical cancer detection, antibiotic resistance modeling, and multi-omics integration, establishing a next-generation paradigm for AI-driven, precision-guided healthcare systems that enhance both human and environmental resilience.