Artificial intelligence and machine learning for colorimetric detections: Techniques, applications, and future prospects

Rapid, low-cost detection of contaminants and quality markers is critical across healthcare, food safety, environmental monitoring, and industrial applications. While traditional laboratory methods remain accurate, they are often slow, expensive, and unsuitable for point-of-care or field use. Colorimetric biosensing offers a simple, affordable, and visually intuitive alternative; however, its dependence on subjective human interpretation introduces bias and limits reproducibility, particularly when subtle color variations arise under different lighting conditions or device types. Recent advances in artificial intelligence (AI), machine learning (ML), and especially deep learning (DL) have transformed these limitations into opportunities by enabling automated, robust, and highly precise analysis. Models such as convolutional neural networks (CNNs) and specialized architectures like ColorNet can directly interpret raw images, extract complex features, and adapt across varied environments, thereby enhancing accuracy and scalability. Through smartphone integration, edge computing, and explainable AI, these systems are now being deployed in diverse real-world scenarios, including biomedical diagnostics, wound and tissue health monitoring, food spoilage and adulteration detection, environmental pollutant sensing, and smart packaging. This review critically examines AI/ML/DL-assisted colorimetric systems, highlights domain-specific applications, and addresses challenges such as dataset generalizability, model interpretability, and regulatory validation, offering practical solutions and future directions for smarter, portable, and accessible biosensing platforms.