Background: Substance misuse continues to rise globally, creating an urgent need for diagnostic tools that are fast, reliable, and easy to use in real-world environments. Traditional laboratory tests depend on fixed cut-off values and centralized processing, which can slow down diagnosis and may miss subtle changes in biomarker levels.
Objective: This work introduces an AI-integrated biosensing system designed to detect substance misuse markers in real time. The goal is to offer a portable, adaptable, and highly accurate solution that can function outside the laboratory.
Methods: Our approach combines functionalized gold–ceria (Au@CeO₂) nanoparticle sensors with machine learning. The sensors generate rich data signals, including electrochemical and optical measurements, which are cleaned and standardized before being analyzed. A hybrid CNN–LSTM neural network is then used to recognize patterns linked to specific substances. The model was trained and tested on a dataset of 5,000 sensor readings per analyte taken from both controlled laboratory runs and synthetic reference samples. Performance was evaluated using 10-fold cross-validation along with common classification metrics.
Results: The system demonstrated an accuracy of over 98%, maintained false positive rates below 1%, and produced results in under two seconds—fast enough for real-time decision-making.
Conclusion: This AI-driven biosensing model provides a practical and highly responsive alternative to conventional laboratory testing. Its portability and speed make it suitable for use in clinical settings, community health programs, roadside testing, and law enforcement. Ultimately, it offers a promising step toward next-generation tools for substance misuse detection and public health monitoring.