Cell movement is central to processes in biology, medicine, and environmental science. Microfluidic technologies have opened new possibilities for studying chemotaxis and motility by creating precise chemical gradients and realistic microenvironments, while allowing direct, real-time imaging under a microscope. At the same time, artificial intelligence (AI) has reshaped how we analyze these behaviors, enabling automated image segmentation, cell tracking, and predictive modeling at scales that were previously impractical. Together, AI and microfluidics form a powerful combination. They offer high-throughput, quantitative insights into single-cell and collective dynamics and drive innovations in areas such as pollutant detection and bioremediation. This review explores recent progress in microfluidic design, AI-based analysis, and portable sensing platforms, and discusses the challenges of data standardization, interpretability, and field deployment. These advances point toward a future where intelligent microsystems play a key role in bioengineering and environmental monitoring.
Zhao et al. (2026) studied this question.