AI-integrated nanomaterial-based biosensors for biomedical diagnostics: engineering design, signal processing, and clinical translation

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.