Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Abstract

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of Sara Momtazmanesh and Ali Nowroozi have contributed equally to this work and share first authorship.

Advancing Rheumatology Practice: Systematic Review of Natural Language Processing Applications

“…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).…”

“…This technological evolution holds particular promise for rheumatology—a field grappling with a diverse range of disorders characterized by significant variability in organs involved, symptoms, treatment responses, and prognosis, which complicates patient management (8–10). NLP, known for its capability to process unstructured clinical data, is gaining recognition in rheumatology as a valuable tool, enhancing both patient care and research methodologies (11–14).…”

A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence

“…When searching for related literature, it may be helpful to use the following search terms as used by Momtazmanesh et al to capture the AI-related literature to your idea: "__disease of interest__" AND ("artificial intelligence" OR "machine learning" OR "machine intelligence" OR "computational intelligence" OR "deep learning" OR "neural network" OR "convolutional network" OR "Bayesian learning" OR "random forest" OR "reinforcement learning" OR "hierarchical learning" OR "computer vision"). 9 Determine the appropriate AI task. Once a need is identified and literature is reviewed, then the appropriate methodology must be selected.…”

“…Images can also be used to predict disease progression such as knee osteoarthritis 36 . Momtazmanesh et al suggest areas for further research including subcortical cysts and joint effusion 9 …”

Original end-to-end smart diagnosis framework of systematic critical quality attributes meets FDA standards of phytomedicine by biosensor and multi-information fusion coupled with AI algorithm

“…19 AI technology is an effective tool for smart diagnosis, which is often applied to process diagnosis and medical diagnosis, etc. 20,21 Their integration can enable the smart diagnosis of quality in the complex production processes of phytomedicine. This integration is of great significance for ETE-SDF to explore the interactive coupling mechanism of multivariate critical variation sources in end-to-end complex processes.…”