Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.
This book has been adapted in large part from the author's doctoral thesis [Shortliffe, l 974b]. Portions of the work appeared previously in Computers And Biomedical Research [Shortliffe, 1973, l 975b], Mathematical Biosciences [Shortliffe, 1975a], and the Proceedings Of The Thirteenth San Diego Biomedical Symposium [Shortliffe, l 974a]. To Stanford's Medical Scientist Training Program, which is supported by the National Institutes of Health Contents
This text is a description of a computer-based system designed to assist physicians with clinical decision-making. This system, termed MYCIN, utilizes computer techniques derived principally from the subfield of computer science known as artificial intelligence (AI). MYCIN's task is to assist with the decisions involved in the selection of appropriate therapy for patients with infections.
MYCIN contains considerable medical expertise and is also a novel application of computing technology. Thus, this text is addressed both to members of the medical community, who may have limited computer science backgrounds, and to computer scientists with limited knowledge of medical computing and clinical medicine. Some sections of the text may be of greater interest to one community than to the other. A guide to the text follows so that you may select those portions most pertinent to your particular interests and background.
The complete book in a single file.
In this book we have presented experimental evidence at many levels of detail for a diverse set of hypotheses. As indicated by the chapter and section headings, the major themes of the MYCIN work have many variations. In this final chapter we will try to summarize the most important results of the work presented. This recapitulation of the lessons learned should not be taken as a substitute for details in the sections themselves. We provide here an abstraction of the details, but hope it also constitutes a useful set of lessons on which others can build. The three main sections of this chapter will reiterate the main goals that provide the context for the experimental work; discuss the experimental results from each of the major parts of the book; and summarize the key questions we have been asked, or have asked ourselves, about the lessons we have learned. If we were to try to summarize in one word why MYCIN works as well as it does, that word would be flexibility.