Role of quantum technology and artificial intelligence for nano-enabled microfluidics

Abstract

Precision medicine aims to recommend tailored treatments for cancer patients, a process facilitated by the integration of artificial intelligence (AI), machine learning (ML), and nanotechnology. This convergence is driving the collection of patient data and enhancing patient outcomes. By utilizing diagnostic nanomaterials, it has become possible to compile a patient's disease profile, enabling the application of various remedial nanotechnologies to aid in the patient's recovery. However, the considerable heterogeneity within cancers presents a significant challenge in devising logical diagnostic and therapeutic strategies, as well as analyzing their outcomes. To bridge this gap, the integration of AI approaches, including pattern investigation and categorization algorithms, has proved invaluable. Applied AI also assumes a pivotal role in the design of nanomedicine, optimizing material characteristics based on projected interactions with biological fluids, target drugs, the vascular system, the immune system, and cell membranes—all of which collectively influence therapeutic efficacy. The synergy of nanotechnology and AI has the potential to completely transform the landscape of precision cancer medicine. Within this chapter, the core tenets of microfluidics and AI are elucidated, alongside an exploration of the imminent impact of nanotechnology. Noteworthy are the diverse applications of machine learning in analyzing microfluidic data, yielding remarkable outcomes. Proposals have been made to synergize microfluidic platforms with closed-loop data-guided models, integrating multimodal monitoring techniques. Beyond establishing a framework for delving into the fundamental principles of materials science and biomedicine, this approach also furnishes insights into domains such as drug discovery, nanomaterials, in vitro organ modeling, and developmental biology. © 2024 Elsevier Inc. All rights reserved.