Journal of Artificial Intelligence and Digital Health

ABSTRACT

Background

Nanobiotechnology integrates nanoscale materials with biological systems, enabling significant advances in targeted drug delivery, biosensing, molecular diagnostics, regenerative medicine, and environmental monitoring. Despite these advances, the complexity of nano–bio interactions and the multidimensional design space of nanomaterials present substantial challenges to conventional experimental approaches. Artificial intelligence (AI), particularly machine learning and deep learning, has emerged as a powerful tool for modelling, predicting, and optimising nano–bio systems, thereby accelerating innovation and improving decision-making across biomedical and environmental applications.

Objective

This review provides a comprehensive overview of AI-integrated nanobiotechnology, with particular emphasis on AI-assisted nanomaterial design, precision medicine, smart diagnostic technologies, cancer therapeutics, environmental and agricultural applications, and food safety. It also critically examines current ethical, biosafety, regulatory, and commercialisation challenges while identifying emerging research directions. 

Methods

I conducted a comprehensive review of peer-reviewed literature published between 2019 and 2026 on the application of artificial intelligence in nanobiotechnology. The review synthesises evidence relating to AI-driven nanomaterial optimisation, intelligent drug delivery systems, nano-biosensors, precision medicine, environmental monitoring, and regulatory developments. Where appropriate, supplementary questionnaire findings are incorporated to provide additional insights into stakeholder perceptions of AI-assisted nanobiotechnology.

Results

The reviewed literature demonstrates that AI has substantially improved the prediction of nano–bio interactions, accelerated nanomaterial design and optimisation, enhanced the performance of intelligent drug delivery systems, and strengthened the analytical capabilities of nano-enabled diagnostic platforms. AI has also expanded opportunities for environmental monitoring, agricultural nanobiotechnology, and pollutant detection through intelligent sensing and predictive modelling. Despite these advances, challenges related to data quality, model interpretability, biosafety assessment, regulatory harmonisation, and ethical governance continue to limit large-scale clinical and industrial implementation.

Conclusion

AI-integrated nanobiotechnology represents a rapidly evolving multidisciplinary field with considerable potential to transform precision medicine, smart diagnostics, environmental sustainability, and advanced healthcare. Continued progress will depend on high-quality data generation, explainable AI models, robust biosafety validation, and internationally harmonised regulatory frameworks that promote safe, transparent, and responsible innovation.

Keywords: Artificial Intelligence; Nanobiotechnology; Machine Learning; Precision Medicine; Smart Diagnostics; Nano-Biosensors; Nanocarriers; Cancer Nanotherapy; Environmental Monitoring; Biosafety