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

Microwell engineering parameters, including cell-loading strategies, well geometry, platform architecture and material selection, directly determine cell capture, spatial organisation and experimental accessibility, and thereby determine what information can be obtained from individual cells. Repositions microwell platforms from a mere spatial confinement tool to a design variable that determines the scope of obtainable data, emphasising that experimental design precedes computational analysis. Review-based argument synthesised from existing microwell platform designs and applications, without new experimental data.

Across applications in cellular behaviour and cell-cell interactions, secretome analysis, genomic and transcriptomic profiling, and drug screening and precision medicine, microwells preserve cell identity and enable spatial, temporal, functional and molecular measurements to be linked within the same workflow. Integrates dispersed application areas into a shared advantage: identity preservation and within-workflow linkage of multi-dimensional measurements. Review-based synthesis covering multiple application directions, though the text provides no specific sample sizes or effect sizes.

The complex datasets generated by microwell platforms are difficult to analyse with conventional approaches, motivating the integration of AI for automated image analysis, cell tracking, phenotype classification, behavioural analysis and prediction of cellular and drug responses. Identifies data complexity as the direct driver for AI integration rather than a mere technological add-on. Review-based argument grounded in the described data characteristics and analytical needs.

The review distinguishes AI approaches directly demonstrated in microwell-based studies from those developed in the broader single-cell field that remain prospective for microwell applications, and discusses future opportunities for multimodal AI, foundation models, large language model agents and autonomous laboratory systems. Provides an evidence-maturity boundary, avoiding the equation of AI results from other single-cell settings with validated capabilities in microwell platforms. Review-based classification and outlook, without specific numbers of validation studies or performance metrics.

The conclusions of this review apply to research settings where microwell platforms serve as tools for single-cell spatial confinement and multi-dimensional measurement, and are intended for researchers working on single-cell analysis, microwell device design, and the use of AI for cell imaging and behavioural analysis. Its value lies in providing a framework for how experimental design determines computable information and in offering directions for subsequent exploration of multimodal AI, foundation models, large language model agents and autonomous laboratory systems.

As a review-based summary, the text provides no specific experimental data, sample sizes or performance metrics, so readers should still watch: which tasks and data scales are covered by AI methods directly demonstrated in microwell studies; the practical feasibility of prospective methods in microwell settings; and the integration pathways for multimodal AI and autonomous laboratory systems in real single-cell workflows.