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


Authors : Faith Ottilia Chimpeni; Allan C. Muzenda

Volume/Issue : Volume 11 - 2026, Issue 1 - January


Google Scholar : https://tinyurl.com/5x7dxwpa

Scribd : https://tinyurl.com/3ujacwdt

DOI : https://doi.org/10.38124/ijisrt/26jan1434

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : 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.

Keywords : Multimodal Artificial Intelligence; Explainable AI; Pan-Gastrointestinal Oncology; Precision Medicine; Treatment Stratification; Early Cancer Detection; Medical Interpretability; Deep Learning.

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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.

Keywords : Multimodal Artificial Intelligence; Explainable AI; Pan-Gastrointestinal Oncology; Precision Medicine; Treatment Stratification; Early Cancer Detection; Medical Interpretability; Deep Learning.

Paper Submission Last Date
28 - February - 2026

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