ONCOLOGY, NUCLEAR MEDICINE AND TRANSPLANTOLOGY
Review Article
Oct 21, 2025 98 48

Transforming Medical Laboratory Science with Vision-Language Models: A Focus on Microscopy in Microbiology, Hematology, and Histopathology

Okoroafor Dorcas Okayo 1,
Nanmet Ephraim Panwal 2,
Okeoma Obiageri Ihuarulam 3,
Markus Saerimam Nzunde 4,
Franca Aminci Nyimwadang 5,
Tosin Ayodeji Oladosu 6 * ,
Adesuwa Benedicta Osunde. 7
1 Department of Biomedical Technology, University of Port Harcourt, Port Harcourt, Rivers State, Nigeria 2 Department of Laboratory Medicine, Haematology / Blood Bank, Sheffield Teaching Hospital NHS Foundation Trust, Sheffield, United Kingdom 3 Department of Medical Laboratory Science, Trinity University Yaba, Lagos, Lagos State, Nigeria 4 Department of Zoology, University of Jos, Jos, Plateau State, Nigeria5 Department of Biochemistry and Molecular Biology, University of Texas Rio Grande Valley, Edinburgh, TX, USA6 Department of Chemistry and Biochemistry, University of Minnesota Duluth, Duluth, MN, USA 7 Department of Microbiology, Oduduwa University Ipetumodu Ile- Ife, Osun State, Nigeria* Corresponding Author
Oncology, Nuclear Medicine and Transplantology, 1(2), 2025, onmt008, https://doi.org/10.63946/onmt/17316
Publication date: Oct 21, 2025
Full Text (PDF)

ABSTRACT

Recent advances in artificial intelligence—particularly Vision–Language Models (VLMs)—offer promising avenues for enhancing microscopic diagnostics. This review synthesizes the current landscape of VLM applications across microbiology, hematology, cytology, and histopathology, spanning tasks such as Gram stain classification, cell-type recognition, feature localization, captioning, and report drafting. We outline how VLMs integrate visual features with domain-specific prompts to support triage, decision support, and quality control, while highlighting opportunities for few-shot and zero-shot generalization to rare findings. In parallel, we compare conventional convolutional pipelines with VLM-enhanced workflows, emphasizing gains in scalability, reproducibility, and explainability through multimodal rationales and grounded visual evidence. Key challenges include data curation and harmonization across laboratories, domain shift from variable staining and optics, bias and safety risks, limited task-relevant benchmarks, and the need for rigorous human-in-the-loop evaluation in clinical contexts. We propose a practical roadmap for deployment—covering dataset governance, prompt and template standardization, uncertainty reporting, and audit trails—alongside research priorities in robust evaluation, privacy-preserving learning, and alignment with clinical guidelines. Overall, VLMs are poised to complement expert microscopy by accelerating routine workflows and improving documentation, provided their adoption is guided by transparent validation and fit-for-purpose governance.

KEYWORDS

Vision-Language Models (VLMs) Medical Laboratory Science Microscopy Artificial Intelligence Diagnostic Automation

CITATION (Vancouver)

Okayo OD, Panwal NE, Ihuarulam OO, Nzunde MS, Nyimwadang FA, Oladosu TA, et al. Transforming Medical Laboratory Science with Vision-Language Models: A Focus on Microscopy in Microbiology, Hematology, and Histopathology. Oncology, Nuclear Medicine and Transplantology. 2025;1(2):onmt008. https://doi.org/10.63946/onmt/17316
APA
Okayo, O. D., Panwal, N. E., Ihuarulam, O. O., Nzunde, M. S., Nyimwadang, F. A., Oladosu, T. A., & Osunde., A. B. (2025). Transforming Medical Laboratory Science with Vision-Language Models: A Focus on Microscopy in Microbiology, Hematology, and Histopathology. Oncology, Nuclear Medicine and Transplantology, 1(2), onmt008. https://doi.org/10.63946/onmt/17316
Harvard
Okayo, O. D., Panwal, N. E., Ihuarulam, O. O., Nzunde, M. S., Nyimwadang, F. A., Oladosu, T. A., and Osunde., A. B. (2025). Transforming Medical Laboratory Science with Vision-Language Models: A Focus on Microscopy in Microbiology, Hematology, and Histopathology. Oncology, Nuclear Medicine and Transplantology, 1(2), onmt008. https://doi.org/10.63946/onmt/17316
AMA
Okayo OD, Panwal NE, Ihuarulam OO, et al. Transforming Medical Laboratory Science with Vision-Language Models: A Focus on Microscopy in Microbiology, Hematology, and Histopathology. Oncology, Nuclear Medicine and Transplantology. 2025;1(2), onmt008. https://doi.org/10.63946/onmt/17316
Chicago
Okayo, Okoroafor Dorcas, Nanmet Ephraim Panwal, Okeoma Obiageri Ihuarulam, Markus Saerimam Nzunde, Franca Aminci Nyimwadang, Tosin Ayodeji Oladosu, and Adesuwa Benedicta Osunde.. "Transforming Medical Laboratory Science with Vision-Language Models: A Focus on Microscopy in Microbiology, Hematology, and Histopathology". Oncology, Nuclear Medicine and Transplantology 2025 1 no. 2 (2025): onmt008. https://doi.org/10.63946/onmt/17316
MLA
Okayo, Okoroafor Dorcas et al. "Transforming Medical Laboratory Science with Vision-Language Models: A Focus on Microscopy in Microbiology, Hematology, and Histopathology". Oncology, Nuclear Medicine and Transplantology, vol. 1, no. 2, 2025, onmt008. https://doi.org/10.63946/onmt/17316

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