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

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.