Artificial intelligence in varicose veins: Comparative insights from other vascular diseases, evidence gaps, and...

This review provides a comprehensive overview of the performance of artificial intelligence (AI) in varicose veins (VV), covering applications in image recognition, postoperative management, risk factor and biomarker analysis, and the use of large language models (LLMs) to aid clinical decision-making. While significant advancements have been made, valuable lessons can be drawn from AI applications in other vascular diseases. Key considerations for advancing AI in VV include ensuring methodological rigor, high quality data, external validation, strict attention to data security and legal standards, and linking AI tools to clinically important outcomes. It is recommended that machine learning models be developed and validated within standardized frameworks that future VV research can adopt. Accordingly, the development of clear guidelines and benchmarks is imperative. LLMs hold promise in relieving clinicians of repetitive routine tasks, thereby enhancing diagnostic accuracy and assisting in treatment planning. Additionally, LLM-based tools could support the curation of medical content on short video platforms and assist clinicians in creating educational materials, ultimately improving patient engagement and understanding.