Advancing microplastic analysis in the era of artificial intelligence: From current applications to the promise of generative AI

Application of Artificial Intelligence to Improve Microplastic Identification

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This review examines how artificial intelligence -- including machine learning and computer vision approaches -- can improve the identification, classification, and measurement of microplastics during environmental sampling and analysis. AI-assisted identification addresses a major bottleneck in microplastic research by reducing the time and expertise required to process large sample volumes, enabling faster and more consistent data generation needed to assess human health exposure at scale.

Unveiling uncertainty in microplastic quantification: Artificial intelligence integrated Raman analysis

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AI-integrated Raman analysis shows heterogeneous real-world MP samples require 40–70% filter coverage for accurate count extrapolation; frequent small-block sampling is most efficient.

Prospective Application of Artificial Intelligence Towards the Detection, and Classifications of Microplastics with Bibliometric Analysis

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Using bibliometric analysis to map the microplastics research landscape, this study proposes integrating AI techniques — including machine learning with FTIR spectroscopy, holographic imaging, and 3D modeling — to overcome the labor-intensive limitations of traditional MP identification and classification methods. Scalable, AI-driven detection tools are critical for accelerating MP monitoring and research, enabling the high-throughput data collection needed to understand population-level exposure and inform regulatory standards.

Intelligent technology-enabled comprehensive research on microplastics: Detection, fate, and ecological effects

Microplastics: a cancer-causing agent for humans and prospects for identification using AI and modern approaches

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Tiny plastic particles called microplastics, smaller than a grain of rice, some invisible to the eye, can get into your body through the air you breathe, the food you eat, and even your skin, where they may trigger harmful biological reactions linked to cancer risk. This review paper pulls together existing research on how these particles behave in the body and highlights promising AI-based tools that could help scientists detect and study microplastics more effectively. The takeaway: microplastics are a growing health concern worth watching, and better detection methods are key to understanding the real risks they pose.