Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review
Radiology reports, as a means of inter-physician and physician-to-patient communication, contain key findings and interpretations from imaging studies that guide diagnosis and treatment. Recent advancements in Artificial Intelligence (AI) technologies and the growing volume of clinical data necessit...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11050386/ |
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| author | Umay Kulsoom Frank G. Glavin Malika Bendechache |
| author_facet | Umay Kulsoom Frank G. Glavin Malika Bendechache |
| author_sort | Umay Kulsoom |
| collection | DOAJ |
| description | Radiology reports, as a means of inter-physician and physician-to-patient communication, contain key findings and interpretations from imaging studies that guide diagnosis and treatment. Recent advancements in Artificial Intelligence (AI) technologies and the growing volume of clinical data necessitate efficient approaches for effective data utilisation. Integrating AI technologies can improve data analysis, enhance diagnostic accuracy, and streamline clinical workflows, reducing the burden on physicians. This Systematic Literature Review (SLR) examines the state-of-the-art of Natural Language Processing (NLP) and Machine Learning (ML) techniques, reviewing their diverse applications in radiology report processing, with a focus on automated disease diagnosis. We explored the commonly used approaches and their impact on diagnostic accuracy. We also looked into the availability of publicly accessible datasets that can be used for ML research within the radiology domain. Despite the promising advancements, challenges remain, such as data quality issues, interpretability of models and integrations of these technologies into clinical workflows. This review provides a thorough overview of the current research in this field by analysing existing findings and highlighting areas of improvement. The goal is to provide insights that can inform future studies in report analysis and improve the use of AI techniques in radiology. |
| format | Article |
| id | doaj-art-5b9065e35ded4509aa9d9882c35b9d74 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5b9065e35ded4509aa9d9882c35b9d742025-08-20T03:29:06ZengIEEEIEEE Access2169-35362025-01-011311221511225410.1109/ACCESS.2025.358272811050386Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic ReviewUmay Kulsoom0https://orcid.org/0000-0002-9264-1512Frank G. Glavin1https://orcid.org/0000-0003-3363-2090Malika Bendechache2https://orcid.org/0000-0003-0069-1860School of Computer Science, University of Galway, Galway, IrelandSchool of Computer Science, University of Galway, Galway, IrelandSchool of Computer Science, University of Galway, Galway, IrelandRadiology reports, as a means of inter-physician and physician-to-patient communication, contain key findings and interpretations from imaging studies that guide diagnosis and treatment. Recent advancements in Artificial Intelligence (AI) technologies and the growing volume of clinical data necessitate efficient approaches for effective data utilisation. Integrating AI technologies can improve data analysis, enhance diagnostic accuracy, and streamline clinical workflows, reducing the burden on physicians. This Systematic Literature Review (SLR) examines the state-of-the-art of Natural Language Processing (NLP) and Machine Learning (ML) techniques, reviewing their diverse applications in radiology report processing, with a focus on automated disease diagnosis. We explored the commonly used approaches and their impact on diagnostic accuracy. We also looked into the availability of publicly accessible datasets that can be used for ML research within the radiology domain. Despite the promising advancements, challenges remain, such as data quality issues, interpretability of models and integrations of these technologies into clinical workflows. This review provides a thorough overview of the current research in this field by analysing existing findings and highlighting areas of improvement. The goal is to provide insights that can inform future studies in report analysis and improve the use of AI techniques in radiology.https://ieeexplore.ieee.org/document/11050386/Artificial intelligencedeep learningmachine learningmultimodalnatural language processingradiology reports |
| spellingShingle | Umay Kulsoom Frank G. Glavin Malika Bendechache Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review IEEE Access Artificial intelligence deep learning machine learning multimodal natural language processing radiology reports |
| title | Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review |
| title_full | Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review |
| title_fullStr | Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review |
| title_full_unstemmed | Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review |
| title_short | Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review |
| title_sort | natural language processing and machine learning for analysis of radiology reports a systematic review |
| topic | Artificial intelligence deep learning machine learning multimodal natural language processing radiology reports |
| url | https://ieeexplore.ieee.org/document/11050386/ |
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