AI-Assisted Molecular Biosensors: Design Strategies for Wearable and Real-Time Monitoring

Area of Science:

• Molecular biosensing
• Biotechnology
• Artificial Intelligence

Background:

• Artificial intelligence (AI) is a transformative tool in molecular biosensing.
• AI enables data-driven optimization in sensor design, signal processing, and real-time monitoring.
• AI enhances biomarker discovery, receptor design, and material engineering for improved biosensor performance.

Purpose of the Study:

• To review advancements in AI-assisted molecular biosensors.
• To highlight sensing strategies and algorithms for wearable and real-time monitoring.
• To discuss challenges and future opportunities in intelligent biosensing.

Main Methods:

• AI-assisted strategies for identifying molecular targets.
• AI-guided design of proteins and aptamers.
• Optimization of plasmonic and nanophotonic structures using AI.
• AI for automatic feature extraction, noise reduction, and data fusion.

Main Results:

• AI significantly enhances the performance of optical, electrochemical, and microfluidic biosensors.
• AI overcomes challenges in complex signals, environmental interference, and device variations.
• AI is crucial for robust and reliable wearable molecular biosensors.

Conclusions:

• AI integration has greatly advanced molecular biosensing capabilities.
• AI-assisted biosensors offer improved sensitivity, specificity, and accuracy.
• Future development opportunities lie in intelligent biosensing technologies for real-time applications.