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.
References :
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- K. Elissa, “Title of paper if known,” unpublished.
- R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press.
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- M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.
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.