Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects

Abstract

Integrating artificial intelligence (AI) with electrochemical biosensors is revolutionizing medical treatments by enhancing patient data collection and enabling the development of advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are significantly more effective. The integration of artificial intelligence is adept at identifying, categorizing, characterizing, and projecting intricate data patterns. As the Internet of Things (IoT), big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. Still, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights the critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology.

Keywords: Artificial neural networks; Electrochemical; Energy saving; Genetic algorithm; Machine learning.

Fig. 1

A schematic illustration of the word cloud serves as the outline in this review for the field of electrochemicals using AI.

Fig. 2

The types of electrochemical biosensors with the general mechanism.

Fig. 3

Paradigm shift in the field of development of electrochemical biosensors in advances.

Fig. 4

A. Functional components of AI-assisted wearable biosensing systems [46]. B. nanozyme–enzyme electrochemical biosensor for sweat lactate monitoring [47]. C. The ‘NutriTrek’ wearable biosensor for metabolic monitoring through sweat biosensing as a mobile application and a smartwatch and wearable system evaluation with custom voltammogram analysis using real-time calibrations [44]. D. A wearable electrochemical fabric for cytokine monitoring [48].

Fig. 5

Machine learning algorithms and their connection to artificial intelligence data treatment.

Fig. 6

Relationship in AI component and Schematic of an ANN-based analytical technique.

Fig. 7

A. The structure of deep learning models. B. AI-coupled electrochemical experiment.

Fig. 8

A. smartphone-based paper microfluidic chip used for the identification and classification of bacterial species [68]. B. Network-based drug repurposing for novel coronaviruses [69]. C. Universal biosensor linked smartphone for co-detection of SARS-CoV-2 viral RNA, antigen, and antibody [70].

Fig. 9

Application of AI-biosensor networks (AIBN).