AIME 89 pp 286-295 | Cite as

Explanation Improvement to Enhance Acceptance of the Plexus System

  • Cornelia van Daalen
  • Rob B. M. Jaspers
Conference paper
Part of the Lecture Notes in Medical Informatics book series (LNMED, volume 38)


PLEXUS is an expert system which assists in the diagnosis and establishment of a treatment plan for patients with a brachial plexus lesion. At present, the quality of advice given by PLEXUS is of a high enough standard to be used regularly by neurologists and neurosurgeons. A good quality of advice, however, is probably not enough to ensure acceptance of the system in a clinical setting.

There are a number of improvements concerning user friendliness which may be incorporated in the system to improve its acceptance. One of the most important factors is the explanation facility. If the advice given by the system is not explained and justified in a way which is related to the internal model and understanding of the physician, the advice will probably not be accepted. A critiquing facility will further improve acceptance of the system, since the advice is compared with the physician’s own ideas. The importance of a user friendly input interface and graphical facilities are also discussed.


Expert System Brachial Plexus Brachial Plexus Injury User Friendliness Strategic Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Bates, M. (1978). The theory and practice of augmented transition network grammars; in: Natural Language Communication with Computers, ed. L. Bolc, Lecture notes in computer science, vol. 63, pp. 191–259, Springer-Verlag.CrossRefGoogle Scholar
  2. Bramer, M.A. (1984). A survey and critical review of expert systems research; in: Introductory Readings in Expert Systems, ed. D. Michie, pp. 3–29, Gordon and Breach Science Publishers.Google Scholar
  3. Daalen, C. van (1987). An expert system for the treatment of brachial plexus injuries (in Dutch), Report A-420, Lab. WMR&CE, Faculty of Mechanical Engineering and Marine Technology, TU-Delft.Google Scholar
  4. Hasling, D.W.; W.J. Clancey, G. Rennels (1984). Strategic explanations for a diagnostic consultation system; in: International Journal of Man-Machine Studies, vol. 20, pp. 3–19.Google Scholar
  5. Jaspers, R.B.M.; F.C.T. van der Helm (1987). Computer aided diagnosis and treatment of brachial plexus injuries; in: Lecture Notes in Medical Informatics, vol. 33, Proc. AIME87, eds. J. Fox et al, pp. 237–246, Springer-Verlag, Berlin.Google Scholar
  6. Jaspers, R.B.M. (1988). PLEXUS: production rules for diagnostics of brachial plexus lesions (in Dutch); in: Proceedings of AI toepassingen88, The Hague, pp. 47–52.Google Scholar
  7. Jonker, W. (1987). DAMOR, a data-model for knowledge representation; in: Report Dept. of Computer Science, TU-Delft.Google Scholar
  8. Lane, C.D.; J.D. Walton, E.H. Shortliffe (1986). Graphical access to medical expert systems: II. Design of an interface for physicians; in: Methods of Information in Medicine, vol. 25, pp. 143–150.Google Scholar
  9. Langlotz, C.P.; E.H. Shortliffe (1983). Adapting a consultation system to critique user plans; in: International Journal of Man-Machine Studies, vol. 19, pp. 479–496.Google Scholar
  10. Lucas, P.J.F.; H. de Swaan Arons (1987). Extensions to the expert system shell Delfi-2; in: 2nd Mini Conference on Expert Systems and Operations Research, Reidel Publishing Company, Dordrecht.Google Scholar
  11. Maguire, M. (1982). An evaluation of published recommendations on the design of man-computer dialogues; in: International Journal of Man-Machine Studies, vol. 16, pp. 237–261.Google Scholar
  12. Miller, P.L. (1986). Expert Critiquing Systems (practice-based medical consultation by computer), Computer and Medicine Series, series ed. B.I. Blum, Springer-Verlag, New York.Google Scholar
  13. Paris, C.L. (1987). Combining discourse strategies to generate descriptions to users along a naive/expert spectrum; in: Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI87), Milan, vol. 2, pp. 626–632.Google Scholar
  14. Rich, E. (1983). Artificial Intelligence, McGraw-Hill.Google Scholar
  15. Swartout, W.R.; S.W. Smoliar (1987). On making expert systems more like experts; in: Expert Systems, vol. 4, no. 3, pp. 196–207.Google Scholar
  16. Teach, R.L.; E.H. Shortliffe (1981). An analysis of physicians attitudes regarding computer-based clinical consultation systems; in: Computers and Biomedical Research, vol. 14, pp. 542–558.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1989

Authors and Affiliations

  • Cornelia van Daalen
    • 1
  • Rob B. M. Jaspers
    • 1
  1. 1.Faculty of Mechanical Engineering and Marine Technology Laboratory for Measurement and Control Man-Machine Systems GroupDelft University of TechnologyDelftThe Netherlands

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