Drug discovery remains a protracted and capital-intensive process, primarily hindered by inefficiencies in drug screening. Microfluidic technology provides a promising approach for in vitro drug screening, enabling physiologically relevant, high-throughput, and cost-effective analysis by mimicking key aspects of cellular microenvironments. The synergistic integration of artificial intelligence (AI) with microfluidics constitutes a pivotal advancement in biomedical analysis. The convergence of the two facilitates automated data analysis, complex pattern recognition, and intelligent experimental control, thereby accelerating drug screening and contributing to enhanced precision. This review systematically presents the latest advancements in AI-assisted microfluidic drug screening, organized by increasing biological complexity: from single-cell analysis (1D), multicellular arrays (2D), and spheroids (3D), to sophisticated Organ-on-a-chip (OoC, 3D+) platforms. We detail how AI algorithms promote screening throughput, sensitivity, and physiological relevance at each scale. Furthermore, we critically discuss the prevailing challenges, including those related to data, model robustness, interpretability, and system integration. Finally, we outline future directions, highlighting the potential of AI-enhanced microfluidics to further advance precision drug discovery and biomedical research. We believe this timely review will offer a useful reference for researchers working in the interdisciplinary field of AI, microfluidics, and pharmacology.

Microfluidics  /  Artificial intelligence  /  Drug screening  /  Organ-on-a-chip  /  Machine learning