Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.

Neurological disorders remain a significant health and economic burden worldwide. Addressing the challenges imposed by existing drugs, associated side- effects, and immune responses in neurodegenerative diseases is essential for developing better therapies. The immune activation in a diseased state has complex treatment protocols and results in hurdles for clinical translation. There is an immense need for the development of multifunctional nanotherapeutics with various properties to address the different limitations and immune interactions exhibited by the existing therapeutics. Nanotechnology has proven its potential to improve therapeutic delivery and enhance efficacy. Promising advancements have been made in developing nanotherapies that can be combined with CRISPR/Cas9 or siRNA for a targeted approach with unique potential for clinical translation. Engineering natural exosomes derived from mesenchymal stem cells (MSCs), dendritic cells (DCs), or macrophages to both deliver therapeutics and modulate the immune responses to tumors or in neurodegenerative disease (ND) can allow for targeted personalized therapeutic approaches. In the present review, we summarize and overview the recent advances in nanotherapeutics in addressing the existing treatment limitations and neuroimmune interactions for developing ND therapies and provide insights into the upcoming advancements in nanotechnology-based nanocarriers.

Technological evolution in wearable sensors accounts for major growth and transformation in a multitude of industries, ranging from healthcare to computing and informatics to communication and biomedical sciences. The major driver for this transformation is the new-found ability to continuously monitor and analyze the patients' physiology in patients' natural setting. Numerous wearable sensors are already on the market and are summarized. Most of the current technologies have focused on electrophysiological, electromechanical, or acoustic measurements. Wearable biochemical sensing devices are in their infancy. Traditional challenges in biochemical sensing such as reliability, repeatability, stability, and drift are amplified in wearable sensing systems due to variabilities in operating environment, sample/sensor handling, and motion artifacts. Enzymatic sensing technologies, due to reduced fluidic challenges, continue to be forerunners for converting into wearable sensors. This paper reviews the recent developments in wearable enzymatic sensors. The wearable sensors have been classified in three major groups based on sensor embodiment and placement relative to the human body: 1) on-body, 2) clothing/textile-based biosensors, and 3) biosensor accessories. The sensors, which come in the forms of stickers and tattoos, are categorized as on-body biosensors. The fabric-based biosensor comes in different models such as smart-shirts, socks, gloves, and smart undergarments with printed sensors for continuous monitoring.