Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review

TL;DR

This review summarizes recent progress in medical vision-language models, focusing on their architectures, datasets, evaluation metrics, challenges, and future directions for healthcare applications.

Contribution

It provides a comprehensive overview of recent advancements in medical VLMs for report generation and VQA, highlighting key techniques and challenges.

Findings

Medical VLMs integrate CV and NLP for healthcare tasks.

Recent models show promising results in medical report generation.

Challenges include clinical validity and patient privacy concerns.

Abstract

Medical vision-language models (VLMs) combine computer vision (CV) and natural language processing (NLP) to analyze visual and textual medical data. Our paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on models designed for medical report generation and visual question answering (VQA). We provide background on NLP and CV, explaining how techniques from both fields are integrated into VLMs to enable learning from multimodal data. Key areas we address include the exploration of medical vision-language datasets, in-depth analyses of architectures and pre-training strategies employed in recent noteworthy medical VLMs, and comprehensive discussion on evaluation metrics for assessing VLMs' performance in medical report generation and VQA. We also highlight current challenges and propose future directions, including enhancing clinical validity and addressing patient privacy concerns. Overall, our review summarizes recent progress in developing VLMs to harness multimodal medical data for improved healthcare applications.