Beyond resolution: AI-powered subcellular bioimaging for next-generation smart diagnostics

ABSTRACT The dynamic landscape of subcellular imaging is entering a transformative era fueled by synergistic advances in fluorescent probe design and artificial intelligence (AI). In this review, we discuss how AI techniques, such as deep learning for image denoising and super-resolution, and neural networks for predicting molecular probe behavior, directly address critical bottlenecks in bioimaging, including phototoxicity, low spatiotemporal resolution, and the quantification of complex dynamic phenotypes. We envision an integrated framework for next-generation "smart bioimaging sensors." This framework establishes a closed loop where AI not only analyzes subcellular images but also guides the design of target-specific probes and optimizes experimental imaging parameters in real-time. Special attention is given to lipid droplet dynamics, mitochondrial potential fluctuations, and membrane remodeling events, as promising indicators in cancer, metabolic, and neurodegenerative diseases. Ultimately, we envision that this closed-loop integration of molecular sensing and computational intelligence will give rise to systems that autonomously interpret and adapt to subcellular dynamics, paving the way for personalized, image-based diagnostics and intelligent therapeutic monitoring.