Integrated artificial intelligence frameworks in single-cell multiomics: From intelligent automation to generative modeling

Single-cell multiomics has transformed biomedical research by enabling the study of gene expression, epigenetic modifications, and protein profiles within individual cells at an unprecedented level of detail. This approach has opened new opportunities for understanding complex biological systems, although significant challenges remain. As datasets increase in size and incorporate multiple modalities, issues such as data sparsity, integration complexity, and the need for scalable experimental methods have become prominent. In this review, recent progress combining computational tools, microfluidic technologies, and clinical applications is examined. The first section focuses on how advanced algorithms have been applied to interpret multimodal data, improve cell type identification, map developmental trajectories, and integrate diverse datasets. New techniques incorporating deep learning architectures, such as variational autoencoders, graph neural networks, and emerging foundation models, are highlighted for their role in enabling robust multimodal integration and predictive analysis. Subsequent sections address innovations in experimental workflows. Microfluidic devices integrated with smart automation and real-time monitoring have improved the reliability and efficiency of single-cell studies. These technical advances have had a tangible impact on translational research. In oncology, immunology, and infectious disease, multiomics-driven insights are informing diagnostic strategies and guiding therapeutic development. Finally, remaining challenges are considered, including regulatory requirements and the incorporation of emerging technologies such as spatial omics. Collectively, these advances point toward a future in which single-cell analysis becomes a cornerstone of precision medicine.