VHealth-MFusion: a fusion-based multimodal deep learning framework for pneumonia tele-diagnosis in virtual hospital environments.

Introduction: Virtual hospital systems support remote triage and diagnosis across distributed healthcare environments by integrating radiographic imaging with structured clinical information for clinical decision making. However, many existing diagnostic frameworks do not fully address real world challenges commonly encountered in scalable virtual healthcare settings. These challenges include severe class imbalance, incomplete clinical attributes, heterogeneous multimodal inputs, and variability in data quality across institutions. Methods: This study introduces Results: Under a controlled dataset-consistent comparison against an established hierarchical multimodal CNN baseline, Discussion: The findings demonstrate that multimodal integration of radiographic imaging and structured clinical information can improve diagnostic robustness and support more reliable Tele-Diagnosis workflows within virtual hospital environments. Overall, the proposed framework contributes a practically oriented multimodal diagnostic architecture designed to facilitate scalable remote clinical services and informed decision making within digitally enabled care systems where imaging and structured clinical information are jointly available.