From Episodic Screening to Continuous Insight: AI Architectures for Colorectal Care.

Abstract

Integrating wearables and clinical data to reshape colorectal cancer (CRC) prevention.Colorectal cancer (CRC) pathways are still dominated by episodic screening through colonoscopy and stool-based tests, despite growing access to consumer sensors and mobile computing. This article presents an end-to-end AI architecture for multimodal colorectal risk stratification that combines traditional screening data with passively collected activity patterns, heart rate variability, stool frequency logs, and nutrition context. We describe a layered design consisting of data ingestion, feature engineering, temporal modeling, risk scoring, and clinician-facing decision support. Implementation patterns are illustrated using cloud-native and edge components suitable for deployment in both high-resource and resource-constrained health systems. The article discusses issues such as bias, data sparsity, longitudinal drift, and integration with existing screening guidelines. As a result, it provides a reference model that future clinical and engineering teams can adapt when building continuous, AI-assisted colorectal screening and monitoring tools.