The work is also of interest because of its relation to psychological studies (Chapter 12) and explanation methodology (Chapter 16).

We have studied difficulties arising in the operations of the "first generation" of AI programs in medicine and have undertaken the development of knowledge representation structures to support needed improvements. The description of a patient in existing programs such as INTERNIST-I (Pople et al., 1975), PIP (see Chapter 6), and MYCIN (Shortliffe, 1976) starts from a single list of findings about the patient. Using a data base of associations between diseases and findings (or rules establishing those connections), these programs form an interpretation of the patient's condition that is essentially a list of possible diseases, ranked by a calculated estimate of likelihood or degree of belief in each. Researchers (Patil, 1979; Pople, 1977; Smith, 1978) have recognized the need to use notions such as causal relationships, temporal patterns, and aggregate disease categories in the description of a program's diagnostic understanding, but the mechanisms provided to do this have been too weak. For example, although causality appears as a term in descriptions in PIP and INTERNIST-I, in both cases its use is limited to guiding the propagation of likelihood measures. These programs fail to capture the human notion that explanation should rest on a chain of cause-effect deduction.

Nor are they claiming that experts do not use general principles for ordering search and selecting alternatives (cf.

Recent research in clinical diagnosis (Barrows et al., 1978; Elstein et al., 1978; McGuire and Bashook, 1978) contributed to a consensus about the general form of the process of clinical diagnostic reasoning. Cues in patient data suggest hypotheses, which are, in turn, tested against subsequent data of the case. The basic hypothetico-deductive process is shared by experienced and inexperienced diagnosticians alike, as are numerous parametric characteristics of the process, such as the percentage of data items to first hypotheses, the average number of hypotheses maintained in active consideration, etc. These studies, however, have generally neglected the content of diagnostic reasoning, that is, the knowledge base of medical subject matter involved in the diagnostic process. Yet, despite prevalent findings of lack of differences in the form of diagnostic reasoning as a function of experience, the few differential findings from these research efforts implicate the importance of the knowledge base.

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.

FIGURE 1-2 How a patient-specific model relates to general disease knowledge.

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