Digital droplets: AI in real-time monitoring of substance abuse – a review

Abstract

Substance abuse affects over 275 million people globally, demanding innovative detection strategies beyond conventional drug testing limitations. This review examines AI-powered analytical platforms integrated with liquid biopsy technologies, termed "digital droplets," for real-time substance abuse monitoring. We systematically analysed advances in AI-enhanced platforms including digital microfluidics, biosensor integration, mass spectrometry-coupled machine learning, and wearable monitoring devices from studies published between 2018 and 2025. AI algorithms demonstrate remarkable capabilities in pattern recognition and multiplexed analyte detection, with deep learning models achieving sensitivity and specificity exceeding 95% in identifying poly-drug use patterns. Digital droplet platforms with convolutional neural networks enable real-time signal processing, reducing detection time from hours to minutes. Wearable biosensors with edge computing facilitate continuous monitoring through non-invasive sampling of sweat, interstitial fluid, and saliva, while predictive models identify relapse risk patterns by integrating longitudinal exposure data with behavioural covariates. These technologies address critical limitations including delayed turnaround times and limited throughput, with applications spanning addiction treatment monitoring, workplace safety, criminal justice supervision, emergency medicine, and precision medicine approaches. Significant challenges persist including standardization across diverse populations, regulatory framework development, data privacy concerns, and ensuring equitable access. Digital droplets represent a paradigm shift in substance abuse monitoring, merging molecular precision with computational power to enable real-time, objective assessment of drug exposure, offering unprecedented opportunities for personalized intervention, treatment optimization, and public health surveillance.