Abstract:

Large language models (LLMs) are emerging artificial intelligence technologies with strong text and image processing capabilities, offering critical support for the intelligent transformation of healthcare and improving clinical efficiency and quality. This review summarizes the current applications, technical features, and future directions of LLMs in cancer diagnosis, focusing on two key scenarios: automated analysis of textual reports (e.g., imaging, pathology, and case summaries) and multimodal diagnosis combining text and medical images. Findings show that LLMs now perform at a level comparable to general resident physicians in cancer diagnosis but are still incapable of making specialized and precise judgments. They also exhibit application-specific traits, such as parameter-efficient models adapted for grassroots-level scenario and divergent versatility in multilingual report analysis. Future efforts should prioritize developing specialized, practical medical LLMs through optimized fine-tuning strategies, construction of high-quality Chinese medical datasets, and integration with vision-language models to promote the clinical application of these models and increase the accessibility of healthcare resources.

Key words: large language models, artificial intelligence, cancer diagnosis, pathology, medical imaging