Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review - Umay Kulsoom; Frank G. Glavin; Malika Bendechache (IEEE Access)
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.