Artificial intelligence-integrated nanobiotechnology for precision medicine, smart diagnostics, and sustainable environmental applications

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 28 | 02 | Page :
    By

    David Sunday ARAOTI,

  1. Independent Researcher, Policy Practitioner, AI & Governance Specialist, Oyo State, Nigeria

Abstract

Background: Nanobiotechnology integrates nanoscale materials with biological systems, enabling breakthroughs in drug delivery, biosensing, and environmental monitoring. However, the complexity of biological interactions and the vast parameter space of nano‑bio interfaces limit conventional design. Artificial intelligence (AI) offers powerful tools for modelling, predicting, and optimising these systems.

Objective: This review provides a systematic, STM‑compliant overview of AI‑integrated nanobiotechnology across three domains: precision medicine (AI‑optimised nanocarriers, personalised therapeutics), smart diagnostics (AI‑powered nano‑biosensors, point‑of‑care devices), and sustainable environmental applications (pollutant detection, agricultural nano‑bio systems). Ethical, biosafety, and regulatory challenges are also addressed.

Methods: We synthesise peer‑reviewed literature (2019–2026) on AI algorithms applied to nanobiotechnology. Six original questionnaires (n = 120–200 each) survey clinicians, researchers, patients, healthcare institutions, the public, and experts on perception, acceptance, safety, and ethical concerns.

Results: AI reduces nanocarrier design iterations by 60‑80% and improves targeting efficiency by >50% in preclinical models. Deep learning‑enhanced nano‑biosensors achieve detection limits as low as 1 fM for cancer biomarkers. Patient acceptance of AI‑guided nanomedicine is moderate (58% willing), but concerns about long‑term safety persist (44% worried). Environmental nano‑bio systems with AI achieve real‑time heavy metal detection at ppb levels.

Conclusions: AI‑integrated nanobiotechnology is poised to transform precision medicine and environmental sustainability. Key barriers include data standardisation, interpretability, and regulatory harmonisation. Future directions include federated learning, explainable AI, and closed‑loop autonomous nano‑bio systems.

Keywords: Artificial intelligence, nanobiotechnology, precision medicine, smart diagnostics, nano‑ biosensors, cancer nanotherapy, environmental monitoring, biosafety

How to cite this article:
David Sunday ARAOTI. Artificial intelligence-integrated nanobiotechnology for precision medicine, smart diagnostics, and sustainable environmental applications. Nano Trends – A Journal of Nano Technology & Its Applications. 2026; 28(02):-.
How to cite this URL:
David Sunday ARAOTI. Artificial intelligence-integrated nanobiotechnology for precision medicine, smart diagnostics, and sustainable environmental applications. Nano Trends – A Journal of Nano Technology & Its Applications. 2026; 28(02):-. Available from: https://journals.stmjournals.com/nts/article=2026/view=249442


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Ahead of Print Subscription Original Research
Volume 28
02
Received 22/05/2026
Accepted 06/07/2026
Published 07/07/2026
Publication Time 46 Days
27

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