Microwell platform for single-cell applications and future integration with artificial intelligence (AI).

Single-cell analysis has become an essential approach for understanding cellular heterogeneity and its implications in biological function, disease progression and therapeutic response. Microwell platforms provide a versatile approach for single-cell analysis by spatially confining individual cells while maintaining access for imaging, perturbation and downstream molecular measurements. This review examines how microwell engineering determines the information that can be obtained from individual cells. We first discuss how cell-loading strategies, well geometry, platform architecture and material selection influence cell capture, spatial organisation and experimental accessibility. Then, review major applications of microwell platforms in cellular behaviour and cell-cell interactions, secretome analysis, genomic and transcriptomic profiling, and drug screening and precision medicine. Across these applications, microwells provide a distinct advantage by preserving cell identity and enabling spatial, temporal, functional and molecular measurements to be linked within the same workflow. However, these capabilities also generate increasingly complex datasets that are difficult to analyse using conventional approaches. We therefore examine the emerging integration of artificial intelligence (AI) for automated image analysis, cell tracking, phenotype classification, behavioural analysis and prediction of cellular and drug responses. Above all, this review distinguishes AI approaches that have been directly demonstrated in microwell-based studies from those developed in the broader single-cell field that remain prospective for microwell applications. Finally, future opportunities for multimodal AI, foundation models, large language model agents and autonomous laboratory systems will be discussed. By linking microwell engineering with AI-driven analysis, this review highlights how experimental design can determine the information available for computational analysis and identifies opportunities to move microwell-based single-cell research from measurement towards prediction and autonomous discovery.