ColonScopeX: Leveraging Explainable Expert Systems with Multimodal Data for Improved Early Diagnosis of Colorectal Cancer

Early detection dramatically improves survival rates: The paper emphasizes that colorectal cancer survival ranges from 80-95% when caught at Stage I, but drops to just 10% at Stage IV. Currently, only 14.4% of UK cases are diagnosed at the earliest stage, highlighting a critical gap that better diagnostic tools could address.

Explainability is essential for clinical adoption: ColonScopeX uses explainable AI (XAI) techniques specifically to make the model's decision-making process transparent and interpretable. This transparency is presented as crucial for "fostering greater trust and understanding" among clinicians—suggesting that even accurate AI systems may face resistance without clear explanations of how they reach their conclusions.

Multiple data sources improve diagnosis: The framework combines blood sample measurements with patient metadata (medication history, comorbidities, age, weight, and BMI) rather than relying on a single data type. This multimodal approach reflects a responsible AI principle: using diverse, complementary information sources can lead to more robust and reliable medical predictions.

AI can address real barriers to early detection: The paper identifies that patients often overlook or hesitate to report colorectal cancer symptoms due to embarrassment. By proposing ColonScopeX as both a triage and population screening tool, the research suggests AI systems could help overcome these behavioral barriers to early diagnosis.

Responsible AI requires clinical integration pathways: The framework's design as a potential "triage tool or screening tool" indicates that responsible AI development in healthcare must consider how systems will actually be deployed and used within existing clinical workflows, not just their technical performance.