JMIR Publications

ABSTRACT

This systematic review aimed to analyze the application and efficacy of large language models (LLMs) in the full-cycle management of colorectal cancer (CRC). CRC diagnosis and treatment face challenges in multi-stage collaboration and integration of large-scale clinical data. While LLMs offer potential solutions, targeted evidence in CRC-specific settings remains insufficient. Following PRISMA guidelines, six databases including PubMed, Embase, and Web of Science were systematically searched up to November 1, 2025. Two reviewers independently screened studies, extracted data, and assessed quality using appropriate tools (QUADAS-2, PROBAST, ROBINS-I) based on study design. A total of 34 studies were included. LLMs were primarily applied in auxiliary diagnosis, information extraction, knowledge-based question answering, treatment decision support, and predictive modeling. GPT-4 achieved a diagnostic accuracy of 97.2% and an F1-score of 84.1% in information extraction, while lightweight or domain-specialized models (e.g., Gemma-2, RoBERTa) performed optimally in specific tasks. Significant heterogeneity was observed across studies due to variations in model types, prompt engineering strategies, and outcome measures. In conclusion, LLMs demonstrate considerable potential in supporting CRC management, particularly in data structuring and decision-making. However, limitations include data bias, model hallucination, and insufficient generalizability. Future research should focus on multicenter prospective validation, improvement of model interpretability, and development of multimodal and domain-adapted LLMs for safe and effective clinical integration.