Where the Machines Fall Short: A Comparative Gap Analysis of AI Methods for Early Colorectal Cancer Screening

Machine learning has been vigorously tackling the problem of early colorectal cancer (CRC) diagnosis, and through the previous four years, several methods with exceptional accuracy have been created. Nevertheless, when considered collectively, these methods have several blind spots that cannot be resolved by a single study. Seven recent works on multimodal fusion, deep learning based on spectroscopy, and blood-based liquid biopsy are examined in this review. For each, we describe the basic algorithm, its key drawback, and the specific gap it leaves unfilled. We observe a recurring pattern: high-accuracy methods almost never offer explanations that a physician can act upon, and they usually employ tissue instead of blood, limited, single-site datasets, or data types too costly for population screening. The most complete of the seven, the Colon Scope X framework is the closest to a deployable design, although it still has problems with demographic breadth, polyp recall, and explanation stability. Instead of another little gain in accuracy, we argue that the field needs a technique that is both non-invasive, inexpensive, demographically robust, and transparent. Instead of focusing on just one statistic, this analysis identifies a deficiency in that combination.