Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.

Global pandemics such as COVID-19 have resulted in significant global social and economic disruption. Although polymerase chain reaction (PCR) is recommended as the standard test for identifying the SARS-CoV-2, conventional assays are time-consuming. In parallel, although artificial intelligence (AI) has been employed to contain the disease, the implementation of AI in PCR analytics, which may enhance the cognition of diagnostics, is quite rare. The information that the amplification curve reveals can reflect the dynamics of reactions. Here, we present a novel AI-aided on-chip approach by integrating deep learning with microfluidic paper-based analytical devices (µPADs) to detect synthetic RNA templates of the SARS-CoV-2 ORF1ab gene. The µPADs feature a multilayer structure by which the devices are compatible with conventional PCR instruments. During analysis, real-time PCR data were synchronously fed to three unsupervised learning models with deep neural networks, including RNN, LSTM, and GRU. Of these, the GRU is found to be most effective and accurate. Based on the experimentally obtained datasets, qualitative forecasting can be made as early as 13 cycles, which significantly enhances the efficiency of the PCR tests by 67.5% (∼40 min). Also, an accurate prediction of the end-point value of PCR curves can be obtained by GRU around 20 cycles. To further improve PCR testing efficiency, we also propose AI-aided dynamic evaluation criteria for determining critical cycle numbers, which enables real-time quantitative analysis of PCR tests. The presented approach is the first to integrate AI for on-chip PCR data analysis. It is capable of forecasting the final output and the trend of qPCR in addition to the conventional end-point Cq calculation. It is also capable of fully exploring the dynamics and intrinsic features of each reaction. This work leverages methodologies from diverse disciplines to provide perspectives and insights beyond the scope of a single scientific field. It is universally applicable and can be extended to multiple areas of fundamental research.

The COVID-19 pandemic has resulted in significant global social and economic disruption. The highly transmissive nature of the disease makes rapid and reliable detection critically important. Point-of-care (POC) tests involve performing diagnostic tests outside of a laboratory that produce a rapid and reliable result. It therefore allows the diagnostics of diseases at or near the patient site. Paper-based POC tests have been gaining interest in recent years as they allow rapid, low-cost detection without the need for external instruments. In this review, we focus on the development of paper-based POC devices for the detection of SARS-CoV-2. The review first introduces the principles of detection methods that are available to paper-based devices. It then summarizes the state-of-the-art paper devices and their analytical performances. The advantages and drawbacks among methods are also discussed. Finally, limitations of the existing devices are discussed, and prospects are given with the hope to identify research opportunities and directions in the field. We hope this review will be helpful for researchers to develop a clinically useful and economically efficient paper-based platform that can be used for rapid, accurate on-site diagnosis to aid in identifying acute infections and eventually contain the COVID-19 pandemic.

The growing demand for non-invasive, real-time health monitoring has driven the development of graphene-based wearable biosensors for point-of-care (POC) diagnostics. This review explores the surface functionalization of graphene and its critical role in enhancing the performance of wearable biosensors for biomarker detection. Leveraging graphene's exceptional electrical, mechanical, and biocompatible properties, we discuss how surface functionalization-such as covalent and non-covalent functionalization, biomolecular probes, and passivation layers-enable highly sensitive and selective detection of biomarkers in biofluids. We categorize biomarkers based on their physical properties and explore various wearable designs, including patches, contact lenses, microneedles, and textiles, highlighting their integration into POC devices. Furthermore, we examine the challenges and opportunities in translating graphene-based sensors from the lab to real-world applications, emphasizing the importance of biocompatibility and surface functionalization for improved performance. By bridging the gap between material science and biomedical engineering, this review provides a roadmap for the development of next-generation graphene biosensors that could revolutionize personalized medicine and point-of-care diagnostics.