Background. The early detection of rheumatic diseases and the treatment to target have become of utmost importance to control the disease and improve its prognosis. However, establishing a diagnosis in early stages is challenging as many diseases initially present with similar symptoms and signs. Expert systems are computer programs designed to support the human decision making and have been developed in almost every field of medicine. Methods. This review focuses on the developments in the field of rheumatology to give a comprehensive insight. Medline, Embase, and Cochrane Library were searched. Results. Reports of 25 expert systems with different design and field of application were found. The performance of 19 of the identified expert systems was evaluated. The proportion of correctly diagnosed cases was between 43.1 and 99.9%. Sensitivity and specificity ranged from 62 to 100 and 88 to 98%, respectively. Conclusions. Promising diagnostic expert systems with moderate to excellent performance were identified. The validation process was in general underappreciated. None of the systems, however, seemed to have succeeded in daily practice. This review identifies optimal characteristics to increase the survival rate of expert systems and may serve as valuable information for future developments in the field. 1. Introduction Rheumatologic diseases manifest themselves in varying combinations of symptoms and signs, particularly at early stages, and therefore make differential diagnosis a challenge, especially for nonrheumatologists including general practitioners. Since diagnosis at an early stage and adequate treatment improve prognosis, assistance in establishing diagnosis is desirable. Given the substantial progress in computer science in the last years, the idea of computers taking the role of diagnostic support is not far-fetched. Software applications have affected decision processes in clinical routine, for example, in controlling depth of anesthesia [1] or in detecting drug interactions [2]. Software tools to support physicians in the diagnostic process have been developed in almost every field of medicine. A widely utilized type is the so-called expert system, defined as artificial intelligence program designed to provide expert-level solutions to complex problems [3]. Figures 1 and 2 give an overview of the concept. Figure 1: Common methodologies for expert systems. Figure 2: Typical structure of a knowledge-based expert system. Based on Buchanan [ 3], the user interface allows the nonexpert to enter the symptoms and findings [ 3] and presents the
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