Automatic Radiology Report Generation: A Comprehensive Review and Innovative Framework

Abstract

scientific research has consistently sought to improve human life quality, with a particular emphasis on advancing healthcare in hospitals and clinics. This study focuses on the development of intelligent systems to improve healthcare by assisting with disease diagnosis and treatment recommendations. Given the precision necessary in the medical area, we look at advanced technologies, specifically deep learning (DL) algorithms that use neural networks (NN) to simulate complex decision-making processes. We present a detailed review of the most recent breakthroughs in Automatic Radiology Report Generation (ARRG) systems, covering the many frameworks and approaches utilized in their implementation, as well as evaluating the most often used datasets and metrics. Additionally, we investigate the relevance of large language models (LLMs) in improving the development and interpretation of radiological reports. A proposed framework, detailing the best practices, future research directions, and implementation strategies, is also presented. This study aims to offer a thorough understanding of ARRG systems, benefiting researchers and practitioners in the field.