A Multidisciplinary Team-Based Large Language Model Framework for Predicting Postoperative Neurological Complications in Acute Type A Aortic Dissection: Model Development and Validation Study.

Published in Journal of medical Internet research. Abstract: Postoperative neurological complications (PNCs) after acute type A aortic dissection (ATAAD) surgery are clinically emergent and require multidimensional perioperative risk assessment. Large language models (LLMs) have shown potential in clinical prediction, but the incremental value of structured multiagent collaboration remains unclear. This study aimed to develop and validate a multidisciplinary team (MDT)-based LLM framework for predicting PNC after ATAAD surgery and to compare its performance with that of traditional machine learning (ML) models and single-agent LLM settings. A retrospective cohort from January 2020 to June 2024 (N=763) was randomly divided into a training set (n=533) and an internal validation set (n=230). A prospective cohort from July 2024 to June 2025 (n=120) was used for prospective validation. The outcome was PNC, defined as stroke, cerebral hemorrhage, paraplegia, or coma. Population-level in-context learning used outcome-stratified summary statistics from the training cohort, including predictor distributions in patients with and without PNC and between-group P values. Two LLMs (DeepSeek-V3 and ChatGPT [GPT-5]) were evaluated under 4 settings: no MDT without in-context learning, MDT without in-context learning, no MDT with in-context learning, and MDT with in-context learning. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, F 1 -score, and Brier score. PNC ...