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International Journal of Rheumatology
Volume 2014, Article ID 672714, 10 pages
http://dx.doi.org/10.1155/2014/672714
Review Article

Computer-Based Diagnostic Expert Systems in Rheumatology: Where Do We Stand in 2014?

1Department of Rheumatology, University Hospital Zurich, Gloriastrasse 25, 8091 Zurich, Switzerland
2Department of Rheumatology, Bethesda Hospital, Gellertstrasse 144, 4020 Basel, Switzerland
3Department of Rheumatology, Zuger Kantonsspital, Landhausstrasse 11, 6340 Baar, Switzerland
4Horten Centre for Patient Oriented Research and Knowledge Transfer, University of Zurich, Pestalozzistraße 24, 8091 Zurich, Switzerland

Received 7 May 2014; Accepted 20 June 2014; Published 8 July 2014

Academic Editor: Ronald F. van Vollenhoven

Copyright © 2014 Hannes Alder et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.