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Ground truth annotations were established using a standardized 15\u2010parameter 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\u2010defined criteria. Receiver operating characteristic analysis demonstrated moderate discriminative performance for viable cells (area under the curve [AUC]\u00a0=\u00a00.68) and strong discriminative performance for necrotic cells (AUC\u00a0=\u00a00.84), reflecting reliable identification of overt necrotic morphology. However, early and late apoptotic states showed poor separability (AUC\u00a0=\u00a00.52\u20130.55) and were inconsistently distinguished, which can be attributed to inadequate spatial\u2013contextual inference within the model. The entire dataset was processed within 2\u00a0h, demonstrating a significant enhancement in analytical throughput relative to manual evaluation. These findings indicate that MLLM\u2010driven cytopathological analysis may serve as an intermediate automation layer between traditional microscopic assessment and fully autonomous deep learning\u2010based image processing systems, facilitating scalable, standardized, and semi\u2010autonomous interpretation of fluorescence imaging data.<\/jats:p>","DOI":"10.1002\/qub2.70042","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T03:10:31Z","timestamp":1777605031000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence\u2010enabled cytopathological interpretation of fluorescence microscopy images via a multimodal large language model"],"prefix":"10.1002","volume":"14","author":[{"given":"Atakan","family":"Tevlek","sequence":"first","affiliation":[{"name":"Department of Medical Biology Faculty of Medicine Atilim University  Ankara 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