Multimodal Medical AI: Fusion, Interpretation, and Clinical Translation

Dear Colleagues,

We are pleased to invite you to contribute to the Special Issue "Multimodal Medical AI: Fusion, Interpretation, and Clinical Translation".

The rapid advancement of artificial intelligence has transformed the way medical data are acquired, integrated, and interpreted. Modern clinical environments generate diverse and heterogeneous information—from radiological images, pathology slides, and biomedical signals to genomics, laboratory results, and electronic health records. To unlock the full potential of this rich ecosystem, multimodal AI aims to fuse complementary medical data sources, enhance interpretability, and deliver clinically actionable insights that can meaningfully support diagnosis, prognosis, and treatment decision-making.

Despite remarkable progress, key challenges remain. Effective multimodal fusion must account for modality imbalance, domain gaps, feature heterogeneity, and missing data. Clinically reliable AI requires transparency, robustness, and generalization across institutions, patient populations, and acquisition conditions. Finally, translating multimodal models into real‑world clinical workflows demands user‑centered design, regulatory readiness, and rigorous validation.

This Special Issue aims to bring together cutting‑edge research that advances multimodal medical AI from foundational methods to clinical deployment. We welcome contributions spanning algorithmic innovation, interpretable modeling, clinical evaluation, and application‑driven case studies. Studies addressing radiology–pathology fusion, cross‑modal alignment, time‑series and signal integration, multi‑omics analysis, and AI‑assisted clinical decision support are particularly encouraged.

We invite original research articles, comprehensive reviews, and clinically oriented studies that demonstrate meaningful progress in multimodal medical AI and its translational impact.

Selected Topics (but not limited to):

• Multimodal fusion of medical images, clinical data, and biosignals
• Cross‑modal learning, alignment, and representation modeling
• Foundation models and large multimodal models (LMMs) for healthcare
• Explainable and trustworthy multimodal AI
• Multimodal clinical decision support systems
• Integration of radiology, pathology, genomics, and EHR data
• Weakly supervised and semi‑supervised multimodal learning
• Multimodal AI for disease diagnosis, prognosis, and treatment planning
• Cross‑institutional generalization and domain adaptation
• Human–AI collaboration and interpretable clinical workflows
• Real‑world clinical validation and translational studies

Prof. Dr. Snehasis Mukhopadhyay
Guest Editor

Manuscript Submission Information

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