Advancement in medical report generation: current practices, challenges, and future directions

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Articles and books
University of La Romana > Research > Scientific Production Cerrado Inglés The correct analysis of medical images requires the medical knowledge and expertise of radiologists to understand, clarify, and explain complex patterns and diagnose diseases. After analyzing, radiologists write detailed and well-structured reports that contribute to the precise and timely diagnosis of patients. However, manually writing reports is often expensive and time-consuming, and it is difficult for radiologists to analyze medical images, particularly images with multiple views and perceptions. It is challenging to accurately diagnose diseases, and many methods are proposed to help radiologists, both traditional and deep learning-based. Automatic report generation is widely used to tackle this issue as it streamlines the process and lessens the burden of manual labeling of images. This paper introduces a systematic literature review with a focus on analyses and evaluating existing research on medical report generation. This SLR follows a proper protocol for the planning, reviewing, and reporting of the results. This review recognizes that the most commonly used deep learning models are encoder-decoder frameworks (45 articles), which provide an accuracy of around 92–95%. Transformers-based models (20 articles) are the second most established method and achieve an accuracy of around 91%. The remaining articles explored in this SLR are attention mechanisms (10), RNN-LSTM (10), Large language models (LLM-10), and graph-based methods (4) with promising results. However, these methods also face certain limitations such as overfitting, risk of bias, and high data dependency that impact their performance. The review not only highlights the strengths and challenges of these methods but also suggests ways to handle them in the future to increase the accuracy and timely generation of medical reports. The goal of this review is to direct radiologists toward methods that lessen their workload and provide precise medical diagnoses. metadata Rehman, Marwareed and Shafi, Imran and Ahmad, Jamil and Osorio García, Carlos Manuel and Pascual Barrera, Alina Eugenia and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, carlos.osorio@uneatlantico.es, alina.pascual@unini.edu.mx, UNSPECIFIED (2024) Advancement in medical report generation: current practices, challenges, and future directions. Medical & Biological Engineering & Computing. ISSN 0140-0118

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Official URL: http://doi.org/10.1007/s11517-024-03265-y

Abstract

The correct analysis of medical images requires the medical knowledge and expertise of radiologists to understand, clarify, and explain complex patterns and diagnose diseases. After analyzing, radiologists write detailed and well-structured reports that contribute to the precise and timely diagnosis of patients. However, manually writing reports is often expensive and time-consuming, and it is difficult for radiologists to analyze medical images, particularly images with multiple views and perceptions. It is challenging to accurately diagnose diseases, and many methods are proposed to help radiologists, both traditional and deep learning-based. Automatic report generation is widely used to tackle this issue as it streamlines the process and lessens the burden of manual labeling of images. This paper introduces a systematic literature review with a focus on analyses and evaluating existing research on medical report generation. This SLR follows a proper protocol for the planning, reviewing, and reporting of the results. This review recognizes that the most commonly used deep learning models are encoder-decoder frameworks (45 articles), which provide an accuracy of around 92–95%. Transformers-based models (20 articles) are the second most established method and achieve an accuracy of around 91%. The remaining articles explored in this SLR are attention mechanisms (10), RNN-LSTM (10), Large language models (LLM-10), and graph-based methods (4) with promising results. However, these methods also face certain limitations such as overfitting, risk of bias, and high data dependency that impact their performance. The review not only highlights the strengths and challenges of these methods but also suggests ways to handle them in the future to increase the accuracy and timely generation of medical reports. The goal of this review is to direct radiologists toward methods that lessen their workload and provide precise medical diagnoses.

Item Type:	Article
Uncontrolled Keywords:	Automated medical health services; Deep learning; Image processing; Public health; Report generation
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Articles and books University of La Romana > Research > Scientific Production
Date Deposited:	22 Jan 2025 23:30
Last Modified:	22 Jan 2025 23:30
URI:	https://repositorio.unic.co.ao/id/eprint/16269

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Advancement in medical report generation: current practices, challenges, and future directions

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