Colorectal cancer organoid technologies offer potential for individualized medication guidance and novel target discoveryNew organoid technology helps researchers study colorectal cancer better

The high heterogeneity and complex microenvironment of colorectal cancer (CRC) represent critical bottlenecks contributing to treatment resistance and clinical translation failure, and the iterative development of organoid technology provides a key driving force for precision oncology. This narrative review systematically outlines the four-stage developmental trajectory of CRC organoid technology—while recognizing that these stages are not strictly sequential replacements but rather long-term coexisting and complementary approaches. The first-generation Matrigel embedding method established the foundation for three-dimensional culture but lacked adequate microenvironment simulation. The second generation achieved preliminary tumor–stromal interactions through optimized culture media and co-culture systems. The third generation integrated engineering technologies such as microfluidic chips and bioprinting, overcoming bottlenecks in high-throughput standardization and dynamic regulation in dedicated platforms. The fourth generation leverages artificial intelligence (AI), achieving substantial advances in intelligent phenotypic analysis, drug-response prediction, and initial exploration of digital twin systems. By comparing differences in heterogeneity retention, throughput, clinical potential, and cost-effectiveness among the four generations, this review emphasizes their core clinical value in individualized medication guidance and novel target discovery. Despite persistent challenges—including insufficient microenvironment simulation, poor AI explainability, and lack of standardization—current evidence suggests that interdisciplinary integration and intelligent platform development can transform organoid technology into a key hub bridging basic research and clinical practice. We therefore recommend accelerating the establishment of standardized organoid biobanks, embedding AI-driven predictive models into clinical workflows, and formulating regulatory frameworks to facilitate the translation of organoid-based precision medicine for colorectal cancer.