Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades

Sidi Liu Yang Liu Ming Li Shuangshuang Shang Yunxiang Cao Xi Shen Chuanbing Huang ^*

• First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, Anhui Province, China

Autoimmune diseases have long been recognized for their intricate nature and elusive mechanisms, presenting significant challenges in both diagnosis and treatment. Despite advancements in comprehending their pathophysiology, there remains a demand for more effective diagnostic and therapeutic approaches. The advent of artificial intelligence technology has opened up new possibilities for understanding, diagnosing, predicting, and managing autoimmune disorders. In the past twenty years, there has been a notable surge in the application of artificial intelligence in this domain. This scholarly investigation delves into the evolution of this field through a bibliometric lens for the first time. By employing bibliometric analysis tools like CiteSpace, HistCite Pro, and VOSviewer, it unveils the global development landscape predominantly led by the United States and China. The research identifies key institutions, such as Brigham & Women’s Hospital, influential journals like the Annals of the Rheumatic Disease, distinguished authors including Katherine P. Liao, and pivotal articles. It visually maps out the research clusters’ evolutionary path over time and explores their applications in patient identification, risk factors, prognosis assessment, diagnosis, classification of disease subtypes, monitoring and decision support, and drug discovery. Moreover, AI has been recognized for its potential in the field of autoimmune diseases, yet it has faced numerous challenges, including insufficient model validation and difficulties in data integration and computational power. Significant advancements have been demanded to enhance diagnostic precision, improve treatment methodologies, and establish robust frameworks for data protection, thereby facilitating more effective management of these complex conditions.