The restricted optical aperture and limited measurement bandwidth of microscopy impose constraints on information acquisition, particularly during the observation of dynamic processes within fine subcellular structures and ultrafast and transient biological events in vivo, and efficient three-dimensional imaging of mesoscopic ex vivo tissues within biological systems. This limitation represents a formidable hurdle in the landscape of multidisciplinary biomedical research. Traditional constraints associated with fluorescence microscopy have prompted studies on innovative principles and methodologies. By integrating artificial intelligence, efforts have been directed toward enhancing the speed and precision of fluorescence microscopy imaging, thereby augmenting information throughput. In this study, a meticulous analysis of problems posed by throughput limitations encountered in the fields of cell biology, developmental biology, and tumor medicine. Through the integration of artificial intelligence, traditional constraints associated with fluorescence microscopy throughput were surmounted. This pioneering approach paves the way for the advancement of physical optics and image processing and greatly contributes to the evolution of biomedical research. This study offers comprehensive insights into intricate phenomena within the realms of life and health, not only holding paramount importance for biomedical exploration but also unveiling promising avenues for future studies and applications.