AI-integrated nanomaterial-based biosensors for biomedical diagnostics: engineering design, signal processing, and clinical translation.
8 affiliations
- 1Faculty of Pharmaceutical Science, Assam down town University (AdtU), Guwahati, Assam, 781026, India. [email protected].
- 2Department of Pharmacy, Faculty of Health Sciences, Victoria University, Victoria Towers, 1-13 Jinja Road, P.O. Box 30866, Kampala, Uganda. [email protected].
- 3Faculty of Pharmaceutical Science, Assam down town University (AdtU), Guwahati, Assam, 781026, India.
- 4Department of Pharmaceutics, Omega College of Pharmacy, Osmania University, Edulabad, Ghatkesar, Hyderabad, Telangana, 501301, India.
- 5Department of Pharmaceutical Technology, School of Health and Medical Sciences, Adamas University, Adamas Knowledge City, Barasat-Barrackpore, District-24 Parganas (North), Kolkata, West Bengal, 700 126, India.
- 6Department of Pharmaceutics, Aligarh College of Pharmacy, Aligarh, Uttar Pradesh, 202001, India.
- 7Department of Mechanical Engineering, School of Engineering and Applied Sciences, Kampala International University, Western Campus, P.O. Box 71, Ishaka-Bushenyi, Uganda.
- 8Department of Electronics and Communication Engineering (ECE), Indian Institute of Information Technology Design and Manufacturing (IIITDM) Kancheepuram, Vandalur-Kelambakkam Road, Melakottaiyur, Chennai, Tamilnadu, 600127, India.
Abstract
Nanobiosensor platforms based on nanomaterials have the potential to change the future of biomedical diagnostics with highly sensitive detection as well as quick responses and various functionalities. This review article will systematically outline the design principles, signal transduction mechanisms, and the biomedical use of nanobiosensors with a strong focus on including AI and wearable technologies to achieve these benefits. Examples of nanomaterials (organic and inorganic, as well as hybrid), including metallic nanoparticles, graphene derivatives, quantum dots, and polymer nanocomposites, allow nanobiosensors to provide increased biosensor performance using mechanisms such as electrochemical transduction, surface plasmon resonance, Förster resonance energy transfer, and surface enhanced Raman scattering. These mechanisms make it possible to detect biomarkers with incredible sensitivity for many different types of diseases, such as all types of infection, cancer, and metabolic disorders. Furthermore, when nanobiosensors are combined with AI and IoT applications, they provide a means of processing data in real time, utilizing predictive analytics and monitoring health based on individualized care. While significant progress has been made over the past decade, barriers to translation remain, including scaling up manufacturing capacity, achieving consistent, reproducible results, validating effectiveness in clinical settings, and obtaining FDA approval for use. This review article will compare various types of biosensing platforms and their respective strengths and limitations; discuss the current challenges faced in developing these platforms; and define future areas of focus for research.
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BibTeX
@article{thalluri2026aiintegrated,
title = {{AI-integrated nanomaterial-based biosensors for biomedical diagnostics: engineering design, signal processing, and clinical translation.}},
author = {Chandrashekar Thalluri and Sarad Pawar Naik Bukke and Jharna Medhi and Mallikarjun Vasam and Ananda Kumar Chettupalli and Neha Gupta and Koushik Narayan Sarma and Milon Selvam Dennison and Bukke Pasunath Naik},
journal = {Biomedical engineering online},
year = {2026},
month = jul,
doi = {10.1186/s12938-026-01608-4},
url = {https://europepmc.org/article/MED/42907992},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - AI-integrated nanomaterial-based biosensors for biomedical diagnostics: engineering design, signal processing, and clinical translation.
AU - Chandrashekar Thalluri
AU - Sarad Pawar Naik Bukke
AU - Jharna Medhi
AU - Mallikarjun Vasam
AU - Ananda Kumar Chettupalli
AU - Neha Gupta
AU - Koushik Narayan Sarma
AU - Milon Selvam Dennison
AU - Bukke Pasunath Naik
PY - 2026
DA - 2026/07/01
JO - Biomedical engineering online
DO - 10.1186/s12938-026-01608-4
UR - https://europepmc.org/article/MED/42907992
AB - Nanobiosensor platforms based on nanomaterials have the potential to change the future of biomedical diagnostics with highly sensitive detection as well as quick responses and various functionalities. This review article will systematically outline the design principles, signal transduction mechanisms, and the biomedical use of nanobiosensors with a strong focus on including AI and wearable technologies to achieve these benefits. Examples of nanomaterials (organic and inorganic, as well as hybrid), including metallic nanoparticles, graphene derivatives, quantum dots, and polymer nanocomposites, allow nanobiosensors to provide increased biosensor performance using mechanisms such as electrochemical transduction, surface plasmon resonance, Förster resonance energy transfer, and surface enhanced Raman scattering. These mechanisms make it possible to detect biomarkers with incredible sensitivity for many different types of diseases, such as all types of infection, cancer, and metabolic disorders. Furthermore, when nanobiosensors are combined with AI and IoT applications, they provide a means of processing data in real time, utilizing predictive analytics and monitoring health based on individualized care. While significant progress has been made over the past decade, barriers to translation remain, including scaling up manufacturing capacity, achieving consistent, reproducible results, validating effectiveness in clinical settings, and obtaining FDA approval for use. This review article will compare various types of biosensing platforms and their respective strengths and limitations; discuss the current challenges faced in developing these platforms; and define future areas of focus for research.
SN - 1475-925X
ER -