Advancing rheumatology with natural language processing: insights and prospects from a systematic review

Objectives Natural language processing (NLP) and large language models (LLMs) have emerged as powerful tools in healthcare, offering advanced methods for analysing unstructured clinical texts. This systematic review aims to evaluate the current applications of NLP and LLMs in rheumatology, focusing on their potential to improve disease detection, diagnosis and patient management.Methods We screened seven databases. We included original research articles that evaluated the performance of NLP models in rheumatology. Data extraction and risk of bias assessment were performed independently by two reviewers, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to evaluate the risk of bias.Results Of 1491 articles initially identified, 35 studies met the inclusion criteria. These studies utilized various data types, including electronic medical records and clinical notes, and employed models like Bidirectional Encoder Representations from Transformers and Generative Pre-trained Transformers. High accuracy was observed in detecting conditions such as RA, SpAs and gout. The use of NLP also showed promise in managing diseases and predicting flares.Conclusion NLP showed significant potential in enhancing rheumatology by improving diagnostic accuracy and personalizing patient care. While applications in detecting diseases like RA and gout are well developed, further research is needed to extend these technologies to rarer and more complex clinical conditions. Overcoming current limitations through targeted research is essential for fully realizing NLP's potential in clinical practice. What does this research mean for patients?Computers are increasingly proficient at interpreting human language, which could enhance the diagnosis and treatment of rheumatic diseases. Our study explores the application of natural language processing (NLP) in rheumatology. We discovered that NLP accurately identifies diseases like rheumatoid arthritis, gout and spondyloarthritis from medical records, potentially allowing for quicker and more precise diagnoses in the future. Advanced NLP models, such as large language models (e.g. Generative Pre-trained Transformers, Bidirectional Encoder Representations from Transformers), can also effectively respond to patients' queries about their conditions and treatments, thereby improving patient education. For instance, they can provide reliable information on medications such as methotrexate. However, the development of NLP for rarer rheumatic diseases remains limited. While promising, this technology requires further study before it can be routinely implemented in medical practice. As research progresses, patients may benefit from more personalized and accurate care.