Artificial Intelligence in Medical Diagnosis

doi:10.1059/0003-4819-108-1-80

Artificial Intelligence in Medical Diagnosis

Cambridge and Boston, Massachusetts

Abstract

In an attempt to overcome limitations inherent in conventional computer-aided diagnosis, investigators have created programs that simulate expert human reasoning. Hopes that such a strategy would lead to clinically useful programs have not been fulfilled, but many of the problems impeding creation of effective artificial intelligence programs have been solved. Strategies have been developed to limit the number of hypotheses that a program must consider and to incorporate pathophysiologic reasoning. The latter innovation permits a program to analyze cases in which one disorder influences the presentation of another. Prototypes embodying such reasoning can explain their conclusions in medical terms that can be reviewed by the user. Despite these advances, further major research and developmental efforts will be necessary before expert performance by the computer becomes a reality.

Article and Author Information

▸ From the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, and Tufts University School of Medicine, Boston, Massachusetts.
Grant support: in part by National Institutes of Health grants R24 RR 01320 from the Division of Research Resources, R01 HL 33041 from the National Heart, Lung and Blood Institute, R01 LM 04493 from the National Library of Medicine; and from the Robert Wood Johnson Foundation and the Commonwealth Fund. The views expressed are those of the authors and do not necessarily represent the views of any of the granting agencies.
▸ Requests for reprints should be addressed to Peter Szolovits, Ph.D.; MIT Laboratory for Computer Science, 545 Technology Square; Cambridge, MA 02139.