Artificial intelligence‐enabled cytopathological interpretation of fluorescence microscopy images via a multimodal large language model

Abstract Quantitative analysis of fluorescence microscopy images remains a significant challenge in cytopathological assessments, as subjective interpretation and inter‐observer variability hinder reproducibility and standardization of classifications. In this study, an artificial intelligence‐assisted analytical framework utilizing a multimodal large language model (MLLM) was employed to classify cytotoxic responses in acridine orange/propidium iodide‐stained MCF‐7 cells subjected to doxorubicin treatment for 24 h. A total of 500 fluorescence images, collected from five experimental groups including a control and four dose‐dependent treatment conditions, were examined to identify distinct morphological indicators of viability, necrosis, and apoptosis. Ground truth annotations were established using a standardized 15‐parameter cytopathological evaluation checklist, conducted by domain experts. The MLLM was subsequently tasked with interpreting the same dataset and generating structured cytopathological outputs aligned with expert‐defined criteria. Receiver operating characteristic analysis demonstrated moderate discriminative performance for viable cells (area under the curve [AUC] = 0.68) and strong discriminative performance for necrotic cells (AUC = 0.84), reflecting reliable identification of overt necrotic morphology. However, early and late apoptotic states showed poor separability (AUC = 0.52–0.55) and were inconsistently distinguished, which can be attributed to inadequate spatial–contextual inference within the model. The entire dataset was processed within 2 h, demonstrating a significant enhancement in analytical throughput relative to manual evaluation. These findings indicate that MLLM‐driven cytopathological analysis may serve as an intermediate automation layer between traditional microscopic assessment and fully autonomous deep learning‐based image processing systems, facilitating scalable, standardized, and semi‐autonomous interpretation of fluorescence imaging data.