Regression vs. Medical LLMs: A Comprehensive Study for CVD and Mortality Risk Prediction

Cardiovascular diseases (CVDs) remain the foremost cause of global morbidity and mortality, driving an urgent need for robust predictive tools that enable early detection and preventive intervention. Traditional regression-based models--such as linear and logistic regression, regression trees and forests, and Support Vector Machines (SVMs)--have long underpinned CVD risk estimation but often assume linear relationships, homogeneous effects across populations, and a limited number of predictors. Recent advances in regression, such as bagging and boosting, as well as Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are increasingly shifting this paradigm. In this paper, we review key developments in the context of both classic regression techniques and recent GenAI approaches, and we put a particular focus on openly available Medical LLMs (MedLLMs) in combination with few-shot prompting and classification finetuning. Based on the LURIC cardiovascular health study, we investigate a broad variety of biomarkers and risk factors under two different cohorts of 3,316 CVD risk patients who underwent coronary angiography in Germany between 1997 and 2000. Our results demonstrate that large, pretrained MedLLMs (70B) achieve up to 82% AUROC for 1-year all-cause mortality (1YM) prediction with optimized few-shot prompting, thus performing competitively with recent regression techniques and state-of-the-art methods from the medical literature such as CoroPredict, SMART and SCORE2. Smaller models (8B) can be finetuned to match or even surpass their larger counterparts as well as commercial models like ClaudeSonnet-4.5 and ChatGPT-5.2. Among all evaluated approaches, the best-performing boosting-based regression technique (CatBoost) and commercial LLM (Gemini-3-Flash) both achieve an AUROC of up to 85%. Further model-calibration and -stratification analyses reveal a systematic mortality over-prediction (ECE: 0.05-0.10) of MedLLMs, while Platt scaling effectively reduces such miscalibrations by 60-90%.