JMIR Publications

ABSTRACT

Background:

The growing complexity of colorectal cancer (CRC) management requires advanced tools for integrating multimodal data and clinical knowledge. Large language models (LLMs) offer a promising approach to address these challenges through sophisticated natural language processing and reasoning capabilities.

Objective:

This systematic review evaluates the current applications, performance, and practical implications of LLMs across the continuum of CRC care, from screening to treatment decision support.

Objective:

This systematic review evaluates the current applications, performance, and practical implications of LLMs across the continuum of CRC care, from screening to treatment decision support.

Methods:

We searched six databases (PubMed, Embase, Web of Science, Scopus, CINAHL, Cochrane) up to November 1, 2025, following PRISMA guidelines. Included studies were original research investigating LLM applications specific to CRC, with extractable outcome data. Quality was assessed using QUADAS-2, PROBAST, and ROBINS-I tools by two independent reviewers.

Results:

Following the screening of 1,261 records, 34 studies met the inclusion criteria, all published between 2023 and 2025. The synthesis highlighted the utility of LLMs in automating data extraction from clinical texts, supporting patient education, aiding diagnostic processes, and assisting in clinical decision-making, with growing evidence of their emerging visual interpretation and multimodal capacities. The effectiveness of these models was significantly influenced by prompt design, which varied from basic zero-shot queries to specialized fine-tuning techniques. While the overall methodological quality of the included studies was deemed adequate, assessments identified recurring concerns regarding insufficient control of biases and inadequate reporting on data security measures.

Conclusions:

LLMs demonstrate tangible potential to augment CRC care, particularly in structuring unstructured data and providing clinical decision support. However, translating this potential into practice requires solutions for domain adaptation, multimodal integration, and rigorous prospective validation to ensure reliability and safety in real-world settings. Clinical Trial: PROSPERO CRD420251248261; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251248261.