Multimodal large language models in brain tumor imaging: clinical applications and future perspectives.

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Abstract

The use of multimodal data is essential for the precise diagnosis and treatment of brain tumors. In this context, multimodal data encompass multisequence magnetic resonance imaging, computed tomography, positron emission tomography, histopathological images, molecular and genomic profiles, structured clinical variables, and radiological reports. With the rapid advancement of artificial intelligence, integrating these heterogeneous data sources has become a central research direction for improving diagnostic accuracy, prognostic assessment, and therapeutic decision-making in neuro-oncology. However, substantial discrepancies exist across data modalities in terms of spatial resolution, semantic representation, and measurement scales, posing significant challenges for effective cross-modal integration. Multimodal large language models (MLLMs) enhance both interpretative and generative capabilities by jointly modeling visual, textual, and structured data, thereby offering a unified framework for addressing these challenges in brain tumor analysis. This review provides a comprehensive overview of MLLMs, covering their methodological foundations, representation learning strategies, and cross-modal alignment mechanisms. We further summarize their applications in both research and emerging clinical settings, including diagnosis support, prognosis prediction, treatment planning assistance, and radiology report generation. Finally, we discuss current limitations, such as data scarcity, interpretability constraints, and clinical deployment barriers, and outline future directions toward robust, explainable, and clinically translatable MLLM systems in neuro-oncology.

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