Revolutionizing Radiology Workflow with Factual and Efficient CXR Report Generation

TL;DR

This paper presents CXR-PathFinder, an advanced AI model for automated chest X-ray report generation that integrates expert feedback and knowledge verification to improve accuracy and reliability in radiology workflows.

Contribution

Introduction of CXR-PathFinder, a novel LLM-based model with Clinician-Guided Adversarial Fine-Tuning and Knowledge Graph Augmentation for improved medical report accuracy.

Findings

Outperforms existing models on clinical accuracy metrics.

Blinded radiologist evaluations favor CXR-PathFinder's utility and correctness.

Achieves a balance of diagnostic fidelity and computational efficiency.

Abstract

The escalating demand for medical image interpretation underscores the critical need for advanced artificial intelligence solutions to enhance the efficiency and accuracy of radiological diagnoses. This paper introduces CXR-PathFinder, a novel Large Language Model (LLM)-centric foundation model specifically engineered for automated chest X-ray (CXR) report generation. We propose a unique training paradigm, Clinician-Guided Adversarial Fine-Tuning (CGAFT), which meticulously integrates expert clinical feedback into an adversarial learning framework to mitigate factual inconsistencies and improve diagnostic precision. Complementing this, our Knowledge Graph Augmentation Module (KGAM) acts as an inference-time safeguard, dynamically verifying generated medical statements against authoritative knowledge bases to minimize hallucinations and ensure standardized terminology. Leveraging a comprehensive dataset of millions of paired CXR images and expert reports, our experiments demonstrate that CXR-PathFinder significantly outperforms existing state-of-the-art medical vision-language models across various quantitative metrics, including clinical accuracy (Macro F1 (14): 46.5, Micro F1 (14): 59.5). Furthermore, blinded human evaluation by board-certified radiologists confirms CXR-PathFinder's superior clinical utility, completeness, and accuracy, establishing its potential as a reliable and efficient aid for radiological practice. The developed method effectively balances high diagnostic fidelity with computational efficiency, providing a robust solution for automated medical report generation.