EchoLLM: extracting echocardiogram entities with light-weight, open-source large language models

Large language models (LLMs) have demonstrated high levels of performance in clinical information extraction compared to rule-based systems and traditional machine-learning approaches, offering scalability, contextualization, and easier deployment. However, most studies rely on proprietary models with privacy concerns and high costs, limiting accessibility. We aim to evaluate 14 publicly available open-source LLMs for extracting clinically relevant findings from free-text echocardiogram reports and examine the feasibility of their implementation in information extraction workflows.

We used 14 open-source LLM models to extract clinically relevant entities from echocardiogram reports (n = 507). Each report was manually annotated by 2 independent health-care professionals and adjudicated by a third. Lexical variance and length of each echocardiogram report were collected. Precision, recall, and F1 scores were calculated for the 9 extracted entities via multiclass classification.

In aggregate, Gemma2:9b-instruct had the highest precision, recall, and F1 scores at 0.973 (0.962-0.983), 0.959 (0.947-0.973), and 0.965 (0.951-0.975), respectively. In comparison, Phi3:3.8b-mini-instruct had the lowest precision score at 0.831 (0.804-0.856), while Gemma:7b-instruct had the lowest recall and F1 scores at 0.382 (0.356-0.408) and 0.392 (0.356-0.428), respectively.

Using LLMs for entity extraction for echocardiogram reports has the potential to support both clinical research and health-care delivery. Our work demonstrates the feasibility of using open-source models for more efficient computation and extraction.