Recent Progress in Artificial Intelligence in Biosensor Development: From Bioprobe Design to Fabrication and Signal Analysis

Abstract

The coronavirus disease 2019 (COVID-19) pandemic highlighted the need for rapid, accurate, and point-of-care diagnostic technologies, accelerating interest in biosensors as next-generation analytical platforms. However, biosensor performance is governed by a connected sequence of processes, including bioprobe–target recognition, sensor fabrication, structural optimization, and signal interpretation. Because these processes involve multiple interacting variables, conventional empirical approaches often have limitations in efficiently optimizing biosensor performance and interpreting complex analytical signals. Artificial intelligence (AI) and machine learning (ML) provide tools to model these relationships and support prediction-guided biosensor development. This review discusses recent progress in AI-assisted biosensor development in three sequential stages. First, AI-assisted bioprobe design is reviewed, including in silico aptamer discovery, smart-SELEX-based aptamer screening, and peptide receptor design for improving molecular recognition. Second, AI-driven sensor fabrication and structural optimization are discussed, focusing on electrochemical feature extraction, paper-based microfluidic device optimization, and optical biosensor parameter prediction. Third, ML-based signal analysis is examined as a strategy for converting complex electrochemical, colorimetric, and optical responses into quantitative analytical outputs. By organizing these examples as a connected workflow rather than as separate applications, this review highlights how AI can link molecular design, device engineering, and signal interpretation to accelerate the development of next-generation biosensors.