Multimodal Explainable Artificial Intelligence for Early Detection, Staging Accuracy, and Treatment Stratification in Pan-Gastrointestinal Oncology

The early diagnosis and accurate staging of pan-gastrointestinal malignancies remain significant clinical challenges due to tumor heterogeneity and the complexity of patient data. This research introduces a multimodal explainable artificial intelligence framework designed to integrate diverse biomedical data streams, including medical imaging, clinical records, and molecular profiling, to enhance diagnostic precision across upper gastrointestinal, hepatopancreatobiliary, and lower gastrointestinal cancers. Unlike traditional black-box models, the proposed approach incorporates interpretable deep learning architectures that provide transparent, feature-based explanations for clinical predictions. By improving staging accuracy and enabling data-driven treatment stratification, the framework supports personalized clinical decision-making and precision oncology. Experimental findings indicate that multimodal integration significantly outperforms single- modality models in early lesion detection and outcome prediction.