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.
David Sunday Araoti· Journal of Artificial Intell...· 0 citations
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.
David Sunday Araoti· Journal of Artificial Intell...· 0 citations
This study investigates the impact of Artificial Intelligence (AI)-driven digital transformation on performance efficiency in Accounting Information Systems (AIS) within emerging economies, with Nigeria as the focal context. The study is anchored on the Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), and Resource-Based View (RBV), which collectively explain technology adoption behavior, diffusion patterns, and performance outcomes.
A cross-sectional descriptive and explanatory survey design was adopted. Primary data were collected from 300 accounting and finance professionals drawn from banking, manufacturing, telecommunications, and public sector organizations across Lagos, Abuja, Port Harcourt, and Ibadan. Data were obtained through a structured Likert-scale questionnaire and analyzed using SPSS version 27.
The findings reveal that AI-driven digital transformation significantly enhances accounting system performance efficiency, particularly in processing speed (β = 0.47, p < .001), reporting accuracy (β = 0.43, p < .001), and real-time financial decision support (β = 0.45, p < .001). Results further indicate that AI integration improves automation of financial workflows, strengthens data consistency, and enhances system responsiveness.
However, the study identifies key barriers including high implementation costs (86.9%), inadequate technical skills (84.1%), cybersecurity risks (82.3%), and infrastructural limitations (75.6%), which collectively slow full-scale AIS transformation.
The study concludes that AI-driven digital transformation is a significant predictor of accounting information system performance efficiency in emerging economies. Theoretically, the study extends TAM and DOI by demonstrating their relevance in AIS transformation, while RBV explains how AI enhances organizational capability. Practically, the study provides actionable insights for policymakers, system developers, and organizational leaders on optimizing AI integration in accounting systems.
David Sunday Araoti· Journal of Artificial Intell...· 0 citations
The increasing prevalence of non-communicable diseases (NCDs) continues to place significant pressure on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure remains limited. Conventional healthcare approaches are often reactive, detecting diseases after substantial progression and reducing opportunities for timely intervention. This challenge highlights the need for predictive, affordable, and data-driven healthcare solutions that can support early diagnosis and prevention.
This study proposes a conceptual framework that integrates metabolomics with artificial intelligence (AI) to support predictive health systems in resource-constrained environments. Metabolomics enables comprehensive characterization of small-molecule metabolites, providing valuable insights into physiological and pathological changes. When combined with machine learning approaches, metabolomic datasets can be analyzed to identify potential biomarkers, classify disease risks, and generate personalized healthcare insights.
The proposed framework presents a multi-layered architecture consisting of metabolomic data acquisition, preprocessing, feature engineering, AI-based predictive modeling, and clinical decision-support outputs. The model emphasizes scalability through the integration of portable diagnostic technologies, cloud-based analytics, edge computing, and decentralized healthcare delivery approaches. It also considers critical implementation challenges, including data harmonization, infrastructure limitations, algorithmic bias, and ethical governance.
Furthermore, the framework highlights the need for empirical validation through pilot studies, technology assessment, and multi-site evaluation to determine its feasibility, reliability, and applicability across diverse healthcare settings. By integrating biological data analysis, computational intelligence, and responsible innovation principles, this study provides a pathway toward accessible predictive and precision public health systems for underserved populations.
Overall, this research contributes to the advancement of AI-enabled healthcare by proposing a scalable and context-sensitive model that bridges metabolomics, artificial intelligence, and healthcare delivery requirements in resource-constrained environments.
David Sunday Araoti· Journal of Artificial Intell...· 0 citations
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