AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic...

Area of Science:

• Nanotechnology and Spectroscopy
• Biomedical Engineering
• Data Science

Background:

• Surface-enhanced Raman scattering (SERS) provides sensitive biomolecular detection but faces challenges in complex biological matrices.
• Real-world samples like serum and plasma contain interfering substances that affect SERS performance.
• Existing SERS methods often struggle with target localization, spectral stability, and data interpretation.

Purpose of the Study:

• To review recent advancements in SERS biosensing from an integrated system perspective.
• To highlight the role of artificial intelligence/machine learning (AI/ML) in improving SERS data analysis.
• To frame AI/ML-assisted SERS as a comprehensive architecture for reliable biosensing.

Main Methods:

• Discusses bio-recognition interfaces for improved target localization.
• Explores signal-transduction strategies including nanotags, immunoassays, CRISPR, nanozymes, and lateral-flow formats.
• Highlights digital SERS for robust, event-based outputs and AI/ML for spectral analysis and decision-making.

Main Results:

• AI/ML significantly enhances the interpretation of complex SERS spectra from biological samples.
• Integrated approaches combining substrate design, interface engineering, and digital SERS improve measurement robustness.
• AI/ML facilitates full-spectrum classification, calibration transfer, and patient-level decision-making.

Conclusions:

• AI/ML-assisted SERS biosensing represents an integrated architecture crucial for clinical translation.
• Future progress relies on validation-ready workflows and robust system integration, not just plasmonic enhancement.
• Standardized and validated SERS systems are essential for reliable operation across diverse samples and clinical settings.