Integration of Nanotechnology, Biotechnology, And Artificial Intelligence for Advanced Biomedical and Environmental Applications
TL;DR
The proposed Nano–Bio–AI framework demonstrates how the synergistic integration of these technologies can support precision medicine through patient-specific therapeutic design and intelligent environmental management through pollutant detection, biodegradation, and ecosystem monitoring.
Abstract
The integration of nanotechnology, biotechnology, and artificial intelligence (AI) represents a transformative interdisciplinary approach for advancing biomedical and environmental applications. This study adopts a PRISMA-guided systematic literature review combined with conceptual framework development to synthesize current evidence on the convergence of these three technological domains and to propose an integrated Nano–Bio–AI framework. Relevant peer-reviewed literature was systematically identified, screened, and synthesized to examine the complementary roles of nanoscale engineering, biological system manipulation, and computational intelligence in addressing contemporary healthcare and environmental challenges. The synthesized evidence indicates that nanotechnology enhances targeted drug delivery, diagnostic sensitivity, and controlled therapeutic release through engineered nanoscale materials. Biotechnology contributes bio-responsive systems, genetic engineering, and molecular manipulation techniques that enable precise biological interactions and adaptive therapeutic responses. Artificial intelligence complements these capabilities by applying machine learning and predictive analytics to large-scale biomedical and environmental datasets, thereby accelerating drug discovery, improving disease prediction, optimizing molecular interactions, and supporting evidence-based decision-making. The proposed Nano–Bio–AI framework demonstrates how the synergistic integration of these technologies can support precision medicine through patient-specific therapeutic design and intelligent environmental management through pollutant detection, biodegradation, and ecosystem monitoring. The review also identifies key implementation challenges, including heterogeneous data integration, nanotoxicity, AI ethics, biosafety, and regulatory governance. Rather than providing empirical validation, the study offers a conceptually grounded framework derived from systematic evidence synthesis to guide future computational, experimental, and clinical research. Overall, the Nano–Bio–AI framework provides a comprehensive foundation for next-generation biomedical innovation and sustainable environmental technologies.