APSIPA Transactions on Signal and Information Processing > Vol 13 > Issue 1

Automatic Medical Report Generation: Methods and Applications

Li Guo, University of British Columbia, Canada, lguo@ece.ubc.ca , Anas M. Tahir, University of British Columbia, Canada, Dong Zhang, University of British Columbia, Canada, Z. Jane Wang, University of British Columbia, Canada, Rabab K. Ward, University of British Columbia, Canada
 
Suggested Citation
Li Guo, Anas M. Tahir, Dong Zhang, Z. Jane Wang and Rabab K. Ward (2024), "Automatic Medical Report Generation: Methods and Applications", APSIPA Transactions on Signal and Information Processing: Vol. 13: No. 1, e24. http://dx.doi.org/10.1561/116.20240044

Publication Date: 17 Oct 2024
© 2024 L. Guo, A. Tahir, D. Zhang, Z. Wang and R. Ward
 
Subjects
Medical image analysis,  Deep learning,  Model choice
 
Keywords
Medical report generationdeep learningartificial intelligencereview
 

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This is published under the terms of CC BY-NC.

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In this article:
Introduction 
Problem Statement 
Methods 
Applications 
Public Dataset 
Evaluation Metrics 
Performance Comparisons 
Future Directions 
Conclusion 
References 

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.

DOI:10.1561/116.20240044