Decoding Applications of Artificial Intelligence in Rheumatology

Abstract

Artificial intelligence (AI) is not a newcomer in medicine. It has been employed for image analysis, disease diagnosis, drug discovery, and improving overall patient care. ChatGPT (Chat Generative Pre-trained Transformer, Inc., Delaware) has renewed interest and enthusiasm in artificial intelligence. Algorithms, machine learning, deep learning, and data analysis are some of the complex terminologies often encountered when health professionals try to learn AI. In this article, we try to review the practical applications of artificial intelligence in vernacular language in the fields of medicine and rheumatology in particular. From the standpoint of the everyday physician, we have endeavored to encapsulate the influence of AI on the cutting edge of medical practice and the potential revolutionary shift in the realm of rheumatology.

Advancing Rheumatology Practice: Systematic Review of Natural Language Processing Applications

“…NLP has the potential to transform rheumatology by enhancing patient care and research (9). With the rise of digital patient health records and advanced diagnostics, there's a surge in patient data.…”

“…AI methods, including NLP, machine learning, and deep learning, are pivotal in harnessing this data for predicting outcomes and guiding clinical decisions (1,8). In rheumatology, AI models have significantly improved the diagnosis of diseases like rheumatoid arthritis using various models (9,10,31). These models aid in screening, disease identification, patient phenotyping in EHRs, assessing treatment responses, and monitoring disease progression (31,32).…”

“…Additionally, AI contributes to risk assessment for comorbidities, drug discovery, and advancing basic science research, making it a powerful tool in modern rheumatology practice (8,9,(31)(32)(33)(34) Despite the benefits, the application of NLP in rheumatology comes with limitations.…”

“…Challenges include data quality dependency, small sample sizes, and the need for wider generalizability in studies (9). Additionally, the complexity of rheumatic diseases necessitates sophisticated and adaptable models, which can accurately reflect diseases' heterogeneity (8,9).…”

ChatGPT Performance in Diagnostic Clinical Microbiology Laboratory-Oriented Case Scenarios

“…Therefore, it is important to continuously evaluate the performance of AI models in such a highly precise healthcare discipline. The reliability of AI-generated health information may prove useful or even essential to health professionals including microbiologists in the near future [33][34][35][36]. While AI-based models such as ChatGPT showed promising perspectives in various healthcare disciplines, their current limitations necessitate continued development and rigorous evaluation to ensure their reliability and accuracy in different clinical settings [2,6].…”