# Automatic captioning for medical imaging (MIC): a rapid review of literature

## TL;DR

This paper reviews recent advances in automatic captioning for medical images, focusing on how AI can help describe medical images for better diagnosis and treatment.

## Contribution

The paper provides a rapid review of the latest achievements in medical image captioning from a medical domain perspective.

## Key findings

- Medical image captioning combines computer vision and natural language processing for clinical applications.
- Current reviews are limited in scope, motivating a broader rapid review of recent advancements.
- The paper highlights datasets, applications, limitations, and future directions in medical image captioning.

## Abstract

Automatically understanding the content of medical images and delivering accurate descriptions is an emerging field of artificial intelligence that combines skills in both computer vision and natural language processing fields. Medical image captioning is involved in various applications related to diagnosis, treatment, report generation and computer-aided diagnosis to facilitate the decision making and clinical workflows. Unlike generic image captioning, medical image captioning highlights the relationships between image objects and clinical findings, which makes it a very challenging task. Although few review papers have already been published in this field, their coverage is still quite limited and only particular problems are addressed. This motivates the current paper where a rapid review protocol was adopted to review the latest achievements in automatic medical image captioning from the medical domain perspective. We aim through this review to provide the reader with an up-to-date literature in this field by summarizing the key findings and approaches in this field, including the related datasets, applications and limitations as well as highlighting the main competitions, challenges and future directions.