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.
Screen printing system has been used as one of the important technologies for developing ready to use portable biosensor devices. Screen printing allows the batch production of sensors on flexible substrates, textile materials and other wearable accessories. Owing to an exceptional performance such as flexibility, low cost, simple fabrication, and ability to translated into industrial scale production, screen printing technique advances the marketing potential of the electrochemical sensors. Unlike the advanced printing techniques, the screen-printing technique is less dependent on surface charge, particle size and the viscosity of the printing ink. In this work, we have attempted to develop a flexible electrochemical sensor for detecting cortisol levels using screen printing technique. Batches of three electrode sensor systems were designed and printed in uniform dimensions (2 cm x 2 cm). The mask for sensor design is fabricated using a transparent polyester sheet with an aid of XY cutter/plotter. Graphene ink was used to create working and counter electrodes, while the Ag/AgCl ink was used to create the reference electrode. A homemade silver ink stabilized with hydrogel network was used to create conductive tracks. A reagent-free detection of cortisol was demonstrated using a redox probe (methylene blue) tagged cortisol specific aptamer immobilized onto the working electrode. The variability between the sensor batches is also investigated. The functionality of the printed sensor was demonstrated for monitoring cortisol levels in human saliva samples. The printed sensing platform can also be integrated with wearable textile platform for monitoring cortisol levels in sweat.
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