Emerging Trends in Artificial Intelligence-Assisted Colorimetric Biosensors for Pathogen Diagnostics

Area of Science:

• Biomedical Engineering
• Infectious Disease Diagnostics
• Artificial Intelligence in Healthcare

Background:

• Infectious diseases pose a significant global health challenge, especially where diagnostic resources are scarce.
• Conventional optical methods for pathogen detection are often slow, error-prone, and require complex equipment.
• Colorimetric biosensors offer a low-cost, user-friendly alternative for point-of-care testing.

Purpose of the Study:

• To review major pathogens, their impact, and current diagnostic limitations.
• To highlight the research gap between traditional optical biosensors and AI-assisted colorimetric approaches.
• To explore recent advancements in AI, particularly machine learning (ML) and deep learning (DL), for pathogen detection.

Main Methods:

• Review of scientific literature focusing on AI models (ML, DL) applied to clinical samples over the last five years.
• Analysis of pathogen characteristics, toxicity, and mortality rates.
• Comparison of conventional optical biosensors with emerging AI-assisted colorimetric methods.

Main Results:

• AI, including ML and DL, enhances diagnostic accuracy but requires pre-training and lacks autonomous adaptation.
• AI-assisted colorimetric biosensors show promise for improved pathogen detection.
• Existing AI models often lack robustness and explainability for clinical use.

Conclusions:

• There is a need for robust, explainable, and smartphone-compatible AI-assisted assays for pathogen detection.
• Future research should focus on developing user-friendly AI tools for rapid and accurate diagnostics.
• AI-assisted colorimetric methods have the potential to revolutionize point-of-care infectious disease diagnostics.