# Artificial Intelligence-Aided Microfluidic Cell Culture Systems

## TL;DR

AI helps improve microfluidic cell culture systems, making them more efficient for drug testing and disease modeling.

## Contribution

This paper reviews how AI enhances microfluidic platforms and organ-on-a-chip systems for biomedical applications.

## Key findings

- AI enables automated phenotyping and real-time control in microfluidic systems.
- AI improves drug screening accuracy and supports adaptive experimental tools.
- AI contributes to precision medicine through patient-derived cell analysis.

## Abstract

Microfluidic cell culture systems and organ-on-a-chip platforms provide powerful tools for modeling physiological processes, disease progression, and drug responses under controlled microenvironmental conditions. These technologies rely on diverse cell culture methodologies, including 2D and 3D culture formats, spheroids, scaffold-based systems, hydrogels, and organoid models, to recapitulate tissue-level functions and generate rich, multiparametric datasets through high-resolution imaging, integrated sensors, and biochemical assays. The heterogeneity and volume of these data introduce substantial challenges in pre-processing, feature extraction, multimodal integration, and biological interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, offers solutions to these analytical bottlenecks by enabling automated phenotyping, predictive modeling, and real-time control of microfluidic environments. Recent advances also highlight the importance of technical frameworks such as dimensionality reduction, explainable feature selection, spectral pre-processing, lightweight on-chip inference models, and privacy-preserving approaches that support robust and deployable AI–microfluidic workflows. AI-enabled microfluidic and organ-on-a-chip systems now span a broad application spectrum, including cancer biology, drug screening, toxicity testing, microbial and environmental monitoring, pathogen detection, angiogenesis studies, nerve-on-a-chip models, and exosome-based diagnostics. These platforms also hold increasing potential for precision medicine, where AI can support individualized therapeutic prediction using patient-derived cells and organoids. As the field moves toward more interpretable and autonomous systems, explainable AI will be essential for ensuring transparency, regulatory acceptance, and biological insight. Recent AI-enabled applications in cancer modeling, drug screening, etc., highlight how deep learning can enable precise detection of phenotypic shifts, classify therapeutic responses with high accuracy, and support closed-loop regulation of microfluidic environments. These studies demonstrate that AI can transform microfluidic systems from static culture platforms into adaptive, data-driven experimental tools capable of enhancing assay reproducibility, accelerating drug discovery, and supporting personalized therapeutic decision-making. This narrative review synthesizes current progress, technical challenges, and future opportunities at the intersection of AI, microfluidic cell culture platforms, and advanced organ-on-a-chip systems, highlighting their emerging role in precision health and next-generation biomedical research.