Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System

This study introduces "RadCouncil," a multi-agent Large Language Model (LLM) framework designed to enhance the generation of impressions in radiology reports from the finding section. RadCouncil comprises three specialized agents: 1) a "Retrieval" Agent that identifies and retrieves similar reports from a vector database, 2) a "Radiologist" Agent that generates impressions based on the finding section of the given report plus the exemplar reports retrieved by the Retrieval Agent, and 3) a "Reviewer" Agent that evaluates the generated impressions and provides feedback. The performance of RadCouncil was evaluated using both quantitative metrics (BLEU, ROUGE, BERTScore) and qualitative criteria assessed by GPT-4, using chest X-ray as a case study. Experiment results show improvements in RadCouncil over the single-agent approach across multiple dimensions, including diagnostic accuracy, stylistic concordance, and clarity. This study highlights the potential of utilizing multiple interacting LLM agents, each with a dedicated task, to enhance performance in specialized medical tasks and the development of more robust and adaptable healthcare AI solutions.

Eye-gaze Guided Multi-modal Alignment Framework for Radiology

In multi-modal frameworks, the alignment of cross-modal features presents a significant challenge. The predominant approach in multi-modal pre-training emphasizes either global or local alignment between modalities, utilizing extensive datasets. This bottom-up driven method often suffers from a lack of interpretability, a critical concern in radiology. Previous studies have integrated high-level labels in medical images or text, but these still rely on manual annotation, a costly and labor-intensive process. Our work introduces a novel approach by using eye-gaze data, collected synchronously by radiologists during diagnostic evaluations. This data, indicating radiologists' focus areas, naturally links chest X-rays to diagnostic texts. We propose the Eye-gaze Guided Multi-modal Alignment (EGMA) framework to harness eye-gaze data for better alignment of image and text features, aiming to reduce reliance on manual annotations and thus cut training costs. Our model demonstrates robust performance, outperforming other state-of-the-art methods in zero-shot classification and retrieval tasks. The incorporation of easily-obtained eye-gaze data during routine radiological diagnoses signifies a step towards minimizing manual annotation dependency. Additionally, we explore the impact of varying amounts of eye-gaze data on model performance, highlighting the feasibility and utility of integrating this auxiliary data into multi-modal pre-training.