Systematic review of natural language processing (NLP) applications in magnetic resonance imaging (MRI)

Abstract

Background: As MRI use grows in medical diagnostics, applying NLP techniques could improve management of related text data. This review aims to explore how NLP can augment radiological evaluations in MRI. Methods: We conducted a PubMed search for studies that applied NLP in the clinical analysis of MRI, including publications up to January 4, 2024. The quality and potential bias of the included studies were assessed using the QUADAS-2 tool. Results: Twenty-six studies published between April 2010 and January 2024, covering more than 160k MRI reports were analyzed. Most of these studies demonstrated low to no risk of bias of the NLP. Neurology was the most frequently studied specialty, with twelve studies, followed by musculoskeletal (MSK) and body imaging. Applications of NLP included staging, quantification, and disease diagnosis. Notably, NLP showed high precision in tumor staging classification and structuring of free-text reports. Conclusion: NLP shows promise in enhancing the utility of MRI. However, there is a need for prospective studies to further validate NLP algorithms in real-time clinical and operational scenarios and across various radiology specialties, which could lead to broader applications in healthcare.