This chapter briefly outlines the difference between traditional instructional programs and ICAI. It then illustrates how GUIDON makes contributions in areas important to medical CAl: interacting with the student in a mixed-initiative dialogue (including the problems of feedback and realism), teaching problem-solving strategies, and assembling a computerbased curriculum. In evaluating GUIDON's performance, one can see the value in the basic idea of formalizing teaching knowledge in procedures that are separate from the knowledge to be taught. However, the program is inherently limited by the MYCIN knowledge base. The rule set is poorly structured, does not contain pathophysiological knowledge for justifying the diagnostic associations, and does not explicitly state the strategies for gathering information and focusing on hypotheses.

Since the early 1970s, researchers in computer-based medical reasoning have begun to recognize the potential benefits of applying symbolic reasoning techniques in clinical domains (see Chapter 3). One such research group is the Heuristic Programming Project at Stanford University. The first medical reasoning program developed by the project, known as the MYCIN system (Shortliffe, 1976), adopted symbolic processing techniques largely in response to a conviction that computer-based consultation systems, in order to be accepted by physicians, should be able to explain how and why a particular conclusion has been derived. Such systems should also be able to incorporate, organize, manipulate, and update large quantities of medical knowledge. Subsequently, a series of additional medical application programs using MYCIN's techniques has been created.

Turing's test (see evaluation) smart instruments, 345

Chapter 10 Chapter 11 Chapter 12 Using Rules The Evolution of MYCIN's Rule Form The Structure of the MYCIN System William van Melle Details of the Consultation System Edward H. Shortliffe Details of the Revised Therapy Algorithm WiUiam J. Clancey Building a Knowledge Base Knowledge Engineering Completeness and Consistency in a Rule-Based System Motoi Suwa, A. Carlisle Scott, and Edward H. Shortliffe Interactive Transfer of Expertise Randall Davis Reasoning Under Uncertainty Uncertainty and Evidential Support A Model of Inexact Reasoning in Medicine Edward H. Shortliffe and Bruce G. Buchanan Probabilistic Reasoning and Certainty Factors J. Barclay Adams 55 67 78 133 149 159 171 209 233 263

J. Barclay Adams, M.D., Ph.D. Associate Physician Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston, Massachusetts 02115 Janice s. Aikins, Ph.D. Research Computer Scientist IBM Palo Alto Scientific Center 1530 Page Mill Road Palo Alto, California 94304 James s. Bennett, M.S. Senior Knowledge Engineer Teknowledge, Inc. 525 University Avenue Palo Alto, California 94301 Sharon Wraith Bennett, R.Ph.

Steps toward a theoretic foundation for complex knowledge-based CAI.

Rephrases the question and gives examples of recognized responses.

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.

This chapter describes an oncology protocol management system, named ONCOCIN after its domain of expertise (cancer therapy) and its historical debt to MYCIN. The program is actually a set of interrelated subsystems, the 1 primary ones being: 1. the Reasoner, a rule-based expert consultant that is the core of the system; and 2. the Interviewer, an interface program that controls a high-speed terminal and the interaction with the physicians using the system. The Interviewer is described in some detail in Chapter 32. This chapter describes the problem domain and the representation and control techniques used by the Reasoner. This chapter is based on an article originally appearing in Proceedings of the Seventh IJCAI, 1981, pp. Used by permission of" International Joint Conferences on Artificial Intelligence, Inc.; copies of the Proceedings are available from William Kaufmann, Inc., 95 First Street, l.os Ahos, CA 94022. Another program, the Intemctor, handles interprocess communication.

Effect of the Tutorial The tutorial experience had a small but significant effect on physicians' Demands and also produced a substantial increase in their knowledge about computing concepts. The results from the Demand-scale were of particular interest. Physicians apparently gained new insights from the tutorial into the potential use and capabilities of medical computing and increased their performance Demands accordingly. These opinions regarding the attributes of acceptable computing systems were surprisingly uniform across physician subgroups both before and after the tutorial. Our interpretation of this result is that physicians are serious about these Demands and that consultation systems are not likely to be clinically effective, regardless of the accuracy of their advice, until these capabilities have been incorporated.