Expert systems in dentistry. Past performance—future prospects

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<ul><li><p>68 </p><p>Review </p><p>J. Dent. 1992; 20: 68-73 </p><p>Expert systems in dentistry. Past performance-future prospects </p><p>S. E. Stheeman, P. F. van der Stelt and P. A. Mileman Department of Oral Radiology, Academic Centre for Dentistry Amsterdam (ACTA), Amsterdam, The Netherlands </p><p>ABSTRACT Expert systems are knowledge-based computer programs designed to provide assistance in diagnosis and treatment planning. They assist the practitioner in decision making. A search of the literature on expert system design for medical and dental applications was carried out. It showed an increase in the number of articles on this subject. Between 1984 and 1991,608 articles have been published in medical journals and two in dental journals. Because it is likely that this development will influence dental practice in the future a critical review of medical literature on the topic has also been carried out. A number of general principles are described to give the dental practitioner some insight into how expert systems work A set of criteria have been formulated from the medical literature which expert systems should meet. These requirements are also applicable to dentistry and may be used to judge dental expert systems. In the last part of the paper the features of several dental expert systems developed in the past decade are described in the light of these criteria. It is concluded that in the future more attention should be paid to the development and evaluation of expert systems in the clinical setting. Only well-designed and properly evaluated expert systems can be expected to earn a place in everyday practice. </p><p>KEY WORDS: Expert systems, Computers, Review </p><p>J. Dent. 1992; 20: 68-73 (Received 18 April 1991; reviewed 28 June 1991; accepted 25 September 1991) </p><p>Correspondence should be addressed to: Dr S. E. Stheeman, Department of Oral Radiology, Academic Centre for Dentistry Amsterdam, Louwesweg 1, 1066 EA Amsterdam, The Netherlands. </p><p>INTRODUCTION </p><p>Medical expert systems which attempt to improve and support diagnostic decision making or treatment planning have been the subject of increasing attention in the last decade. Expert systems are knowledge-based computer programs designed to provide assistance in diagnosis and treatment planning. They assist the practitioner in decision making. In medicine, working expert systems have been in existence since the 1970s (Bramer, 1981). The development of these systems has become widely recognized as a research priority (Adams et al., 1986). Some problems concerning implementation of these systems however remain, such as their ease of use and the lack of commonly accepted agreement in clinical termin- ology used by the medical profession (de Dombal, 1987). At this time only a small number of medical expert systems are in use in clinical practice (Lucas and Janssens, 1985). </p><p>Modern medicine has become so complex that it is difficult for the doctor to keep up with all current developments (de Dombal, 1988a). There are great </p><p>@ 1992 Buttenvorth-Heinemann Ltd. 0300-57 12/92/020068-06 </p><p>differences in diagnostic problem-solving techniques among medical practitioners (Taylor et al., 1971). The number and type of additional tests requested vary greatly among medical students near the end of their training (de Vries RobbC, 1978). Expert systems can retain and process more items of information and knowledge than human beings (Kinney, 1987). Large amounts of data can be integrated and processed by them correctly (Erdman, 1987). They are therefore an excellent way of making available to professionals, with a core of basic knowledge, large amounts of specialized knowledge about a particular field. In addition to acting as a reference, using an expert system can be an excellent way to manipulate and store patient information needed for diagnosis, treatment planning and evaluation. </p><p>Computer consultative aids may decrease the cost of health care because they are less expensive than human consultants and because they may also reach clinical decisions more quickly (Taylor et al., 1971) with less expensive supplementary diagnostic tests. They can also improve diagnostic accuracy because of their systematic </p></li><li><p>200 </p><p>r </p><p>1984 1985 1986 1987 </p><p>Year </p><p>1988 1989 1990 </p><p>Fig, 1. Number of publications on medical and dental expert systems 1984-90. ??, Evaluation; 0, general. </p><p>and thorough approach (Erdman, 1987). Expert systems are capable of improving the quality of the clinical decision-making process (de Dombal et al., 1972; Gorry and Silverman, 1978). </p><p>From a review of the literature it is apparent that there is an increase in the number of publications concerning expert systems (Fig 1). </p><p>The use of expert systems in dentistry is likely to play an increasingly important role in the future. The working field of the practitioner is an always expanding one in which it is difficult to keep up with the constant flow of information. New techniques are constantly being introduced and procedures that are complex or were not possible in the past are now undertaken by the dentist in everyday practice. The field of the dental specialist is constantly shifting and referrals to the specialist can be expected to be an area of future innovation. The available knowledge about disease, diagnosis, treatment and sub- sequent prognosis is growing by the day, making the working field more specialized. More than ever there is a need for detailed information and fast information processing. Dentists are already adopting computerized administrative systems and the proportion of dentists presently using them is growing (Stheeman, 1989). It seems likely that dental expert systems in the future will combine administrative packages in such a way that they are available on demand in the surgery. However, at present, the lack of agreement on medical terminology makes the transfer of information between two or more programs very difficult. It is suggested that it is likely that the development and use of dental expert systems will increase substantially. It will be important for the dental practitioner to grasp how such systems work. The rest of this article will therefore review a number of articles about </p><p>Stheeman et al.: Expert systems in dentistry 69 </p><p>dental expert systems that were published in the past 5 years. First however a few general principles will be explained concerning the use of existing expert systems in medicine. </p><p>EXPERT SYSTEMS IN MEDICINE </p><p>The knowledge needed to develop an expert system is often ill defined (Shortliffe, 1986). To collect the detailed information needed to build an expert system is often a tedious and time-consuming task (Shortliffe, 1986). In addition, knowledge in medicine is of an imprecise nature and involves a degree of uncertainty, for example, in the relationship between symptoms and diagnosis. In order to design expert systems that can make decisions using this imprecise knowledge many approaches have been developed. These fall into two major groups. First there are methods that calculate what diagnosis is statistically the most likely or what decision is best in the given circumstances. Secondly, symbolic reasoning can be used to reach a conclusion. In practice a combination of both approaches is often used. </p><p>Probabilistic algorithms </p><p>Epidemiological information about many diseases has been published. This information is numerical and can easily be used in conjunction with a computer. </p><p>One of the best known ways of dealing with probabilities is known as analytical decision making and it is based on Bayes theorem (Bayes, 1763). Using this method the probability that a given patient is suffering from a particular disease can be calculated from the probability that symptoms present in the patient coincide with that disease given that the prevalence of disease and symptoms in the population are known. For each disease built into the program this probability can be calculated. The assumption is made that the patient suffers from the disease with the most likely outcome. This is a commonly used approach in decision-making analysis in dentistry (Mileman et al., 1986; Tulloch and Antczak-Bouckoms, 1987; Maryniuk et al., 1988). </p><p>The main difficulty in applying Bayes theorem is the large amount of data needed to determine conditional probabilities used in the formula (ONeil, 1990). The prevalence for each disease and the probabilities of their occurrence in a patient with certain symptoms must be known. The method faulters when one or more diseases are characterized by only a few features (de Vries and de Vries Robbe, 1985). Therefore the Bayesian approach works best with well-defined problems. Expert systems using decision analysis have been shown to be difficult to transfer from one patient population to another (de Dombal et al., 1981). Poor user acceptance may occur in spite of a highly valid system (de Dombal, 1988b). Examples of such programs are described by Nugent et al. (1964) Wameretal. (1961)andFitzgeralderal. (1966).De </p></li><li><p>70 J. Dent. 1992; 20: No. 2 </p><p>Dombal(1988a) however published the experiences of 13 years of successful use of an expert system for the computer-assisted diagnosis of acute abdominal pain based on the method described. </p><p>The pattern match approach is another probabilistic method sometimes used in expert system design. Here the set of symptoms and clinical findings from a patient is compared with the set of symptoms and findings typical for a disease. This is done for every disease known to the program. The disease or condition that best matches the patients symptoms and clinical findings is considered to be the most likely diagnosis. Compared to the analytical decision-making approach, this method is easier to implement because it only requires knowledge concerning the symptoms not their prevelance. It is used in a few programs developed in dentistry either in a role supporting probabilistic calculations (White, 1989) or as the main part of the expert system (Abbey, 1987). </p><p>Symbolic reasoning </p><p>It is possible to represent expert knowledge as a set of rules. These have the basic form: IF situation THEN action/conclusion. Rules of this kind are called production rules. </p><p>Because of the imprecise nature of medical knowledge these rules are often combined with certainty factors. Such a rule might have the following form: IFsymptoms THEN possible/probable/most likely cause or another possible form of the same information is: IF symptoms THEN cause 0.7. In the latter example of a production rule the certainty factor is 0.7 (see Fig 2). It represents a degree of belief in the conclusion about the cause of the symptoms. A combination of symbolic reasoning and quasi- probabilistic reasoning has been developed in which a hypothesis may be proven, rejected or accepted (Shortliffe and Buchanan, 1975). </p><p>The main disadvantage of this kind of reasoning is that these rules often are not explicit in the medical literature. It often takes experts to define them (de Vries and de Vries Robbt, 1985) which is very time consuming. It is also very difficult to make the rules watertight. A number of high- performance systems have been developed using these types of rules. Perhaps the best known of them all is MYCIN which advises physicians about selection of antimicrobial therapy (Shortliffe, 1974). </p><p>In addition to defining knowledge in the form of production rules there are also systems designed in which the factual knowledge in the computer program is made completely independently of the general reasoning strategies. The philosophy behind such a system is that the knowledge can be made available in this way for many different tasks. An important example of a system with such a broad scope is INTERNIST-I (Pople et al., 1975; Miller and Pople, 1982). </p><p>Not only the medical data but sometimes also the data entered by the user of the expert system is of an imprecise nature. For instance, suppose when an expert system asks </p><p>IF: 1) The infection is primary-bacteraemia, and 2) The site of the culture is one of the sterile sites, </p><p>and 3) The suspected portal of entry of the organism is </p><p>the gastrointestinal tract, THEN: There is suggestive evidence (0.7) that the identity </p><p>of the organism is bacteroides. </p><p>Fig. 2. An example of a production rule from MYCIN. </p><p>the user whether the heart rate of a patient is elevated, each user may have a different rate which he calls elevated. Therefore the answer will be imprecise or fuzzy. This kind of imprecise data can be quantified by using so called truth values as percentages for any possible answer from the user. This might mean, for example, that a heart rate of 110 beats per minute is considered definitely elevated (meaning that the truth value will be 100 per cent). At a heart rate of 85 however, the truth of it being elevated might be 50 per cent because it is in a range of what may be considered to be normal heart rate. When the answers of the user have been quantified they can be used in decision making, for example, diagnosis. This kind of reasoning is called fuzzy logic (Zadeh, 1983) and it is used in one of the dental expert systems which will be discussed below. </p><p>What demands should be made of expert systems? </p><p>From the experiences with expert systems in medicine it is possible to define a set of requirements for the use of expert systems in everyday dental or oral surgery practice, as follows: </p><p>1. The system should be easy to use and respond quickly (Timpka, 1987). </p><p>2. The system should easily tit into the current clinical diagnostic and therapeutic strategy without demanding a change of approach from the user in diagnostic or therapeutic problem solving. </p><p>3. The system should be robust enough to be able to function with incomplete information; it should be able to render an intermediate result if not all the data is available, and suggest what missing information should be obtained and in what way. </p><p>4. The user should always be in control of the process of consulting an expert system (de Vries RobbC et al., 1989). </p><p>5. The system should have a clear explanatory facility (Erdman, 1987; Teach and Shortliffe, 1981; Wallis and Shortliffe, 1982) preferably explaining its conclusions in terms of knowledge instead of showing the rules that led to its conclusions (de Vries Robbe et al., 1989); the program could suggest literature references about the subject under consideration. </p><p>6. The specificity and sensitivity of the results of applying the program to a diagnostic problem should be as high as possible. </p><p>7. The knowledge used should be generally accepted among experts in the field. </p></li><li><p>Stheeman et al.: Expert systems in dentistry 71 </p><p>8. The effectiveness of the system should have been evaluated by potential users from different geographical settings. </p><p>9. The system should be designed to be able to be integrated into administrative systems in current use (Timpka, 1987) or be an easy to implement reference system. </p><p>EXPERT SYSTEMS IN DENTISTRY </p><p>In dentistry several papers have been published concern-...</p></li></ul>

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