Applications of Artificial Intelligence and Machine Learning in Microbiome and Colorectal Cancer Research: Diagnostic Advances, Prognostic Tools, and Forensic Implications

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming microbiome and colorectal cancer (CRC) research by enabling high-throughput data analysis and predictive modelling. This review highlights the current applications of AI/ML tools, such as Convolutional Neural Networks, Random Forest classifiers, and Support Vector Machines, in CRC diagnostics and microbiome profiling. It discusses how AI-integrated endoscopic and imaging systems improve polyp detection accuracy and reduce diagnostic delays. The manuscript also introduces the novel use of AI and microbial fingerprints in forensic science, including postmortem interval estimation and individual identification. Lastly, emerging trends in microbiotabased precision medicine and ethical considerations surrounding AI deployment are explored. These insights underscore AI/ML’s potential in reshaping clinical diagnostics, prognostics, and forensic practices related to CRC. This review emphasizes the translational impact of AI/ML in CRC, from bench to bedside to the courtroom, highlighting both current challenges and future research directions.