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

Area of Science:

• Biotechnology
• Artificial Intelligence
• Sensor Technology

Background:

• The COVID-19 pandemic underscored the need for advanced point-of-care diagnostic technologies.
• Biosensor development involves complex, interconnected processes like bioprobe design, fabrication, and signal interpretation.
• Traditional empirical methods struggle with optimizing biosensor performance and analyzing complex signals.

Purpose of the Study:

• To review recent advancements in artificial intelligence (AI)-assisted biosensor development.
• To highlight how AI and machine learning (ML) can streamline biosensor design and analysis.
• To showcase AI's role in linking molecular design, device engineering, and signal interpretation for next-generation biosensors.

Main Methods:

• Review of AI applications in bioprobe design (in silico aptamer discovery, smart-SELEX, peptide receptor design).
• Examination of AI in sensor fabrication and structural optimization (electrochemical features, paper-based microfluidics, optical parameters).
• Analysis of ML for signal interpretation (electrochemical, colorimetric, optical responses).

Main Results:

• AI facilitates in silico aptamer discovery and enhances aptamer screening via smart-SELEX.
• AI optimizes sensor fabrication, including paper-based microfluidic devices and optical biosensor parameters.
• ML effectively converts complex biosensor signals into quantitative analytical outputs.

Conclusions:

• AI and ML offer powerful tools to overcome limitations in conventional biosensor development.
• Integrating AI across the entire biosensor workflow (design, fabrication, analysis) accelerates innovation.
• AI-assisted development is crucial for creating next-generation biosensors for rapid and accurate diagnostics.