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

**Authors:** Iryna Hartsock, Ghulam Rasool  
**arXiv:** [2403.02469](https://arxiv.org/abs/2403.02469)  
**Date:** 2024-03-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.02469v2/

## Key points

- Authors Iryna Hartsock and Ghulam Rasool review medical vision-language models (VLMs) combining computer vision (CV) and natural language processing (NLP) for analyzing medical data.
- Focus on tailoring VLMs for healthcare applications, particularly in medical report generation and visual question answering (VQA).
- Integration of NLP and CV methodologies into VLMs to learn from multimodal data sources.
- Exploration of medical vision-language datasets, analysis of architectures, and examination of pre-training strategies in cutting-edge medical VLMs.
- Discussion on evaluation metrics used to assess VLM performance in tasks like medical report generation and VQA.
- Addressing current challenges in the field and proposing future directions to enhance clinical validity and address patient privacy concerns.
- Highlighting the potential of VLMs to transform healthcare applications by leveraging multimodal medical data.

## Summary

In their paper titled "Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review," authors Iryna Hartsock and Ghulam Rasool delve into the realm of medical vision-language models (VLMs) that amalgamate computer vision (CV) and natural language processing (NLP) to scrutinize visual and textual medical data. The review focuses on recent advancements in tailoring VLMs for healthcare applications, specifically honing in on models crafted for medical report generation and visual question answering (VQA). The authors provide a comprehensive background on NLP and CV, elucidating how methodologies from both domains are seamlessly integrated into VLMs to facilitate learning from multimodal data sources. They meticulously explore various facets of medical vision-language datasets, conduct in-depth analyses of architectures, and dissect pre-training strategies utilized in cutting-edge medical VLMs. Furthermore, the paper delves into a detailed discussion on evaluation metrics employed to gauge the performance of VLMs in tasks such as medical report generation and VQA. Moreover, the review sheds light on current challenges encountered in this domain while also proposing future directions aimed at enhancing clinical validity and addressing concerns related to patient privacy. By summarizing recent progress in developing VLMs tailored for harnessing multimodal medical data, the authors underscore the potential these models hold for revolutionizing healthcare applications. The paper serves as a valuable resource for researchers and practitioners seeking insights into the evolving landscape of VLMs within the healthcare sector.