Radiology report generation using automatic keyword adaptation, frequency-based multi-label classification and text-to-text large language models.

Journal: Computers in biology and medicine
Published Date: Jul 3, 2025

Abstract

BACKGROUND: Radiology reports are essential in medical imaging, providing critical insights for diagnosis, treatment, and patient management by bridging the gap between radiologists and referring physicians. However, the manual generation of radiology reports is time-consuming and labor-intensive, leading to inefficiencies and delays in clinical workflows, particularly as case volumes increase. Although deep learning approaches have shown promise in automating radiology report generation, existing methods, particularly those based on the encoder-decoder framework, suffer from significant limitations. These include a lack of explainability due to black-box features generated by encoder and limited adaptability to diverse clinical settings.

Authors

  • Zebang He
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China. Electronic address: zebang.he@connect.polyu.hk.
  • Alex Ngai Nick Wong
    DOBI Medical International Inc., Hangzhou, China. Electronic address: axwong93@gmail.com.
  • Jung Sun Yoo
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China. Electronic address: jungsun.yoo@polyu.edu.hk.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals