Abstract: Objective To explore the feasibility and potential of large language models （LLMs） in streamlining risk assessment process of coronary artery bypass grafting （CABG）, with a focus on interpretability and clinic applicability. Methods Using clinical data from 1 372 CABG patients at two cardiac centers, the OpenAI GPT-o3 model was used to process preoperative information to create a simplified risk assessment model via few-shot prompting. This LLM-based model's performance was compared to the European System for Cardiac Operative Risk Evaluation Ⅱ （EuroSCOREⅡ） for consistency and accuracy. Results LLMs excelled at detecting high surgical risks and generating structured risk assessments, surpassing EuroSCOREⅡ in area under the curve （AUC） of receiver operating characteristic, sensitivity, and specificity, thus better identifying high-risk patients. Conclusion LLMs can create and simplify surgical risk assessment models with strong clinical use, potentially complementing or replacing current cardiac surgery risk assessment tools and offering new options for personalized risk evaluation and decision support in clinical settings.

Key words: Large language models, Coronary artery bypass grafting, Surgery risk assessment, Artificial intelligence