MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis

TL;DR

MiniGPT-Med is a versatile vision-language model tailored for radiology that integrates image and text data to improve diagnostic accuracy across multiple imaging modalities and tasks.

Contribution

It introduces MiniGPT-Med, a novel large language model-based system capable of performing diverse radiology tasks with superior accuracy, bridging gaps in AI-assisted diagnosis.

Findings

Achieves state-of-the-art performance in medical report generation, surpassing previous models by 19% accuracy.

Demonstrates high effectiveness in disease grounding and visual question answering tasks.

Enhances diagnostic efficiency across X-rays, CT scans, and MRIs.

Abstract

Recent advancements in artificial intelligence (AI) have precipitated significant breakthroughs in healthcare, particularly in refining diagnostic procedures. However, previous studies have often been constrained to limited functionalities. This study introduces MiniGPT-Med, a vision-language model derived from large-scale language models and tailored for medical applications. MiniGPT-Med demonstrates remarkable versatility across various imaging modalities, including X-rays, CT scans, and MRIs, enhancing its utility. The model is capable of performing tasks such as medical report generation, visual question answering (VQA), and disease identification within medical imagery. Its integrated processing of both image and textual clinical data markedly improves diagnostic accuracy. Our empirical assessments confirm MiniGPT-Med's superior performance in disease grounding, medical report generation, and VQA benchmarks, representing a significant step towards reducing the gap in assisting radiology practice. Furthermore, it achieves state-of-the-art performance on medical report generation, higher than the previous best model by 19\% accuracy. MiniGPT-Med promises to become a general interface for radiology diagnoses, enhancing diagnostic efficiency across a wide range of medical imaging applications.