Automatic Medical Report Generation: Methods and Applications

TL;DR

This review paper analyzes recent advances in automatic medical report generation using AI, highlighting challenges, applications, datasets, evaluation metrics, and future research directions to address radiologist workload and diagnostic accuracy.

Contribution

It provides a comprehensive overview of AMRG methods from 2021 to 2024, including solutions, applications, datasets, metrics, and future challenges in the field.

Findings

Identification of key techniques improving model performance

Compilation of publicly available datasets for AMRG

Discussion of unresolved issues and future research directions

Abstract

The increasing demand for medical imaging has surpassed the capacity of available radiologists, leading to diagnostic delays and potential misdiagnoses. Artificial intelligence (AI) techniques, particularly in automatic medical report generation (AMRG), offer a promising solution to this dilemma. This review comprehensively examines AMRG methods from 2021 to 2024. It (i) presents solutions to primary challenges in this field, (ii) explores AMRG applications across various imaging modalities, (iii) introduces publicly available datasets, (iv) outlines evaluation metrics, (v) identifies techniques that significantly enhance model performance, and (vi) discusses unresolved issues and potential future research directions. This paper aims to provide a comprehensive understanding of the existing literature and inspire valuable future research.