Quantifying the Impact of AI on Colorimetric Biosensor Performance: A Focused Metadata Review

Abstract

Colorimetric biosensors offer low-cost diagnostics but often suffer from subjective interpretation, environmental variability, and limited quantification. Artificial intelligence (AI) has emerged as a powerful solution, enabling automated analysis of chromogenic outputs captured via smartphones or imaging systems. This meta-analysis reviews 32 studies (2022–2025) applying AI to colorimetric biosensing, comparing performance across model types, sensor formats (e.g., paper, wearable, tube-based), input modalities (e.g., RGB, absorbance), and analyte classes. Key metrics include classification accuracy, regression strength (R²), and limit of detection (LOD), benchmarked against non-AI and conventional methods.AI-enhanced platforms  consistently improved accuracy, with context-specific gains in R² and LOD, especially for weak or overlapping signals.

Smartphone-based RGB systems dominated but required calibration strategies such as CNN-GRU correction and illumination adjustment. Despite promising results, most studies lacked external validation and relied on supervised learning with small datasets. Semi-supervised approaches and standardized benchmarks are needed to ensure generalizability. Beyond analytical metrics, AI offered faster readouts, automated interpretation, and support for multiplexed sensing. Future directions include integrating augmented reality for enhanced usability and applying AI to sensor design and optimization. Collectively, these advances position AI-enhanced colorimetric biosensors as scalable, field-ready diagnostic tools with growing potential for clinical and environmental deployment.