Basic Science Reasoning and Clinical Reasoning Intertwined: Epistemological Analysis and Consequences for Medical Education

Lorenzo Magnani

Department of Philosophy

University of Pavia -- Pavia

ITALY

lorenzo@philos.unipv.it

The aim of this paper is to emphasize the distinction between basic medical science (and reasoning) and clinical science (and reasoning) in order to illuminate some basic philosophical and cognitive issues in medical education. The Kunhian concept of exemplar refers to the field of growth of scientific knowledge and in this sense is related to the "anti-theoretical" emphasis on problem solving performance. In cognitive science this (and similar) types of postpositivistic objections to the formalistic excess of the neopositivistic tradition are exploited to stress the relevance of the distinction between theories and their domains of application. This objection is exploited to stress the difference between established bodies of scientific knowledge and their processes of discovery and/or application and, in medical knowledge, between clinical reasoning (situated, concerned with attributes of people) and basic science reasoning (unsituated, concerned with attributes of entities such as organs, bacteria, viruses).

Exploiting the theoretical consequences of the previous analysis I will try to answer some questions: What is the role of problem solving in teaching and learning, as different from conventional basic science-centred education? Is it relevant, in medical education, an epistemological and logical awareness of the main methodological topics? Finally, the analysis of the significance of abduction in a unified epistemological model of medical reasoning is exploited to individuate the proper ontological level dealing with the entities and relationships belonging to the dynamism of the underlying domain knowledge (for instance biomedical physics) and the consequences for medical education.

From an epistemological point of view (Schaffner, 1986) biomedical sciences can be considered as a set of partially overlapping models (sometimes built at the cross-roads of several disciplines) of semi-independent phenomena dealing with prototypical cases. The role of generalizations in biomedical sciences is to use explicitly exemplars - exemplars are identified by Kuhn (1970) as the accepted, prototypical problems that can be encountered both when learning a discipline and in discussion of its contemporary research - and to capture causal relations between them, whereas the role of generalizations in the physical sciences is to give abstract laws relating to several exemplars. In the clinical biomedical sciences exemplars also concern individual’s abnormalities: "This implies that an important, perhaps implicit, component of medical theory involves models of normative biomedical behavior. Since that, too, may be based on sets of exemplars, we see the possibility that clinical medicine, if a scientific theory, is a theory based on models of models - clearly not a straightforward product of axioms of biology" (Patel, Evans and Groen, 1989a, p. 56).

The Kunhian concept of exemplars refers to the field of growth of scientific knowledge and in this sense is related to the "anti-theoretical" emphasis on problem-solving performance:

In cognitive science this (and similar) type of postpositivistic objection to the formalistic excess of the neopositivistic tradition in philosophy of science is exploited to stress the relevance of the distinction between theories and their domains of application. This objection is exploited to emphasize the difference between established bodies of scientific knowledge and their processes of discovery and/or application and, in medical knowledge, between clinical reasoning (situated, concerned with attributes of people) and basic science reasoning (unsituated, concerned with attributes of entities such as organs, bacteria, or viruses). There have been many experimental studies (see Sect. 3) in cognitive psychology to elucidate the precise role of basic science in medical problem-solving in order to determine: 1) to what extent basic science and clinical knowledge are complementary; 2) what basic science contributes to medical problem solving; and 3) whether basic science knowledge contributes to medical expertise (Patel, Evans and Groen, 1989a, p. 62); see also (Groen and Patel, 1988; Patel, Evans and Kaufman, 1990). The distinction between basic medical science (and reasoning), and clinical science (and reasoning) is also included in the general problem of medical education (see Sect 4).

The aim here is to outline some basic philosophical issues that may help to clarify the problem of medical education, at least from a theoretical point of view. The problem of "teaching" science is a very old topic of philosophical reflection. Plato’s Meno is a dialogue about whether virtue can be taught (Turner, 1989). The problem is related to the "Meno paradox", stated by Plato in the dialogue and discussed by Simon in 1976 (Simon, 1976 and 1977, pp. 338-341), and to the issue of "tacit knowledge" which was introduced by Polanyi (1966). The slave boy in the dialogue is brought in to make a related point: "Socrates establishes a) that the boy cannot correctly answer the question (‘cannot tell’, in Polanyi’s language), of how much larger the sides of a square with double the area of another square will be, and b) that the boy thinks he knows that if a square has twice the area the sides will also be doubled. He then leads the boy through a series of inferences, each of which the boy could ‘tell’, at least could assent in response to Socrates’ ‘questions’ formulating those influential steps, and that he could correctly multiply and add when asked" (Turner, 1989, p. 85). These queries leads the boy to the correct answer. The story of Meno’s slave can be looked at from the point of view of an epistemological argument about the paradoxical concept of "problem solving". Polanyi thinks the Meno story shows that if all knowledge is explicit, i.e., capable of being clearly stated, then we cannot know a problem or look for its solution. It also shows that if problems nevertheless exist, and discoveries can be made by solving them, we can know things that we cannot express: "to search for the solution of a problem is an absurdity; for either you know what you are looking for, and then there is no problem; or you do not know what you are looking for, and then you cannot expect to find anything" (Polanyi, 1966, p. 22).

Plato’s solution of this epistemological impasse is the very classic philosophical scenario of the doctrine of reminiscence: Socrates’ teaching is in reality leading the slave to discover the knowledge he already possesses in his spirit. Simon provides a computational solution of the paradox in modern problem solving terms: "our ability to know what we are looking for does not depend upon our having an effective procedure for finding it: we need only an effective procedure for testing candidates" (Simon, 1977, p. 339). If it is possible to have an effective procedure for testing, and an effective procedure for generating candidates, we will have a "problem", i.e. an unsolved problem, where we nevertheless "know what we are looking for" without actually possessing it. As Turner states, "In the case of Goldbach’s conjecture, we can set up the following procedures: generate even numbers, generate numbers named by their prime factors, and make judgements of equality. The problem then can be defined as follows: ‘find a number k generated by the first procedure that does not belong to the numbers generated by the second procedure’. Thus the example fits the ‘general scheme for defining problem solutions prior to finding them’" (Turner, 1989, p. 86).

I digressed in order to illustrate a prototypical "cognitive" story, from philosophical to knowledge engineering outcomes. Socrates teaches the slave some geometric issues in a problem-oriented fashion, not a theorematic one (but this is before Euclid’s Elements). He shows the slave some inferential routines and subroutines (for recognizing numerical inconsistency or for calculating area, for instance) for generating and testing (in Simon’s terms) that enable him to self-program (or "learn") and solve the problem, thus coming to know new geometric notions.

These observations delineate the centrality of the concept of problem-solving in teaching and learning. There is no longer room for a philosophical doctrine of reminiscence. New developments consist of benefiting from recent rational clarifications of problem-solving and problem-oriented knowledge due to artificiale intelligence (AI) and cognitive science. Thus the philosophical story above introduces the main methodological issues in medical education. In medical training the following ideas need to be emphasized and added to conventional curricula:

To exploit the educational consequences of the previous analysis I will try to answer some questions: What is the role of problem solving in teaching and learning, as different from conventional basic science-centred education? Is it relevant, in medical education, an epistemological and logical awareness of the main methodological topics?

To answer these questions it is necessary: 1) to analyze the significance of abduction in a unified epistemological model of medical reasoning to individuate the proper ontological level dealing with the entities and relationships belonging to the dynamism of the underlying domain knowledge (basic - for example biomedical physics -, or clinical); 2) to illustrate the relationships and compatibility between my epistemological framework and certain cognitive models of physician’s expertise involving the concepts of forward and backward reasoning.

The concept of abduction is philosophically very powerful: I will try to show in the following section its efficacy in unifying many intellectual areas devoted to the clarification of problem solving processes and medical reasoning. In my opinion these abductive schemes may form a forceful language capable of establishing a relatively solid and objective framework that increases the intelligibility of many cognitive phenomena.

The present section deals with the concept of abduction which proposes a unified epistemological model of medical reasoning.

First, it is necessary to show the connections between abduction, induction, and deduction and to stress the significance of abduction to illustrate the problem solving process. I have developed with others (Lanzola, Stefanelli, Barosi and Magnani, 1990; Ramoni, Stefanelli, Magnani and Barosi, 1992; Stefanelli, Lanzola, Barosi and Magnani, 1988) an epistemological model of medical reasoning (diagnosis, therapy, monitoring), called the Select and Test Model (ST-MODEL, see also Magnani, 1992) which can be described in terms of the classical notions of abduction, deduction and induction: it describes the different roles played by such basic inference types in developing various kinds of medical reasoning (diagnosis, therapy planning, monitoring). It is thus connected with cognitive models of medical reasoning (see Sect. 3) and provides an abstract representation - an epistemological architecture - of the control knowledge embedded in a medical Knowledge-Based System (KBS) (see Magnani, 1992).

To illustrate from the field of medical knowledge, the discovery of a new disease and the definition of the manifestations it causes can be considered as the result of the creative abductive inference previously described. Therefore, creative abduction deals with the whole field of the growth of scientific knowledge. However, this is irrelevant in medical diagnosis where instead the task is to select from an encyclopedia of pre-stored diagnostic entities, diseases, and pathophysiologic states, which can be made to account for the patient’s condition. On the other hand, diagnostic reasoning also involves abductive steps, but its creativity is much weaker: it usually requires the selection of a diagnostic hypothesis from a set of pre-enumerated hypotheses provided from established medical knowledge. Thus, this type of abduction can be called selective abduction (Magnani, 1988). Selective abduction implies uncertainty and corresponds to the heuristic classification problem-solving model proposed by Clancey (1985), it deals with a kind of rediscovery, instead of a genuine discovery.

"Visual abduction", a special form of abduction, occurs when hypotheses are instantly derived from a stored series of previous similar experiences. In this case there is no uncertainty. It covers a mental procedure that tapers into a non-inferential one, and falls into the category called "perception" (Anderson, 1987, pp. 38-44). We should remember, as Peirce noted, that abduction plays a role even in relatively simple visual phenomena. Many visual stimuli are ambiguous, yet people are adept at imposing order on them: "We readily form such hypotheses as that an obscurely seen face belongs to a friend of ours, because we can thereby explain what has been observed" (Thagard, 1988, p. 53). Philosophically, perception is viewed by Peirce as a fast and uncontrolled knowledge- production procedure (Anderson, 1987).. Perception, in fact, is a vehicle for the instantaneous retrieval of knowledge that was previously structured in our mind through inferential processes. By perception, knowledge constructions are so instantly reorganized that they become habitual and diffuse and do not need any further testing. As stated above, in my epistemological model perception is considered as a form of visual abduction (Magnani, Previde Massara and Civita, 1994).

Induction in its widest sense is an ampliative process of the generalization of knowledge. Peirce distinguished three types of induction and the first was further divided into three sub-types. A common feature of all kinds of induction is the ability to compare individual statements: using induction it is possible to synthesize individual statements into general laws (types I and II), but it is also possible to confirm or discount hypotheses (type III). Clearly I am referring here to the latter type of induction, that in my model is used as the process of reducing the uncertainty of established hypotheses by comparing their consequences with observed facts.

Deduction is an inference that refers to a logical implication. Deduction may be distinguished from abduction and induction on the grounds that only in deduction is the truth of inference guaranteed by the truth of the premises on which it is based. All these distinctions need to be exemplified. To describe how the three inferences operate, it is useful to start with a very simple example dealing with diagnostic reasoning:

By deduction we can infer (3) from (1) and (2); by induction we can go from a finite set of facts, like (2) and (3), to a universally quantified generalization, like the piece of hematologic knowledge represented by (1). Starting from knowing - selecting - (1) and observing (3) we can infer (2) by performing a selective abduction. Such an inference is not affected by uncertainty, since the manifestation (3) is pathognomonic for beta- thalassemia. However clinicians very often have to deal with manifestations which can be justified by different diagnostic hypotheses.

Thus, selective abduction is the making of a preliminary guess that introduces a set of plausible diagnostic hypotheses, followed by deduction to explore their consequences, and by induction to test them with available patient data, (1) to increase the likelihood of a hypothesis by noting evidence explained by that one, rather than by competing hypotheses, or (2) to refute all but one. (Fig. 1).

INSERT FIGURE ABOUT HERE

Figure 1. The epistemological model of diagnosticreasoning.

If during this first cycle new information emerges, hypotheses not previously considered can be suggested and a new cycle takes place: in this case the nonmonotonic character of abductive reasoning is clear. There are two main epistemological meanings of the word abduction (Thagard, 1992): (1) abduction that only generates plausible hypotheses (selective or creative) - and this is the meaning of abduction accepted in my epistemological model - and (2) abduction considered as inference to the best explanation, that also evaluates hypotheses (on this subject also see below Sect. 3). In the latter sense the classical meaning of abduction as inference to the best explanation (for instance in medicine, to the best diagnosis) is described in my epistemological model by the complete abduction - deduction - induction cycle. All we can expect of my "selective" abduction, is that it tends to produce hypotheses that have some chance of turning out to be the best explanation. Selective abduction will always produce hypotheses that give at least a partial explanation and therefore have a small amount of initial plausibility.

In this respect abduction is more efficacious than the blind generation of hypotheses.

In accordance with the epistemological model previously illustrated, medical reasoning may be broken down into two different phases: first, patient data is abstracted and used to select hypotheses, that is hypothetical solutions of the patient’s problem (selective abduction phase); second, these hypotheses provide the starting conditions for forecasts of expected consequences which should be compared to the patient’s data in order to evaluate (corroborate or eliminate) those hypotheses which they come from (deduction-induction cycle).

Diagnosis, therapy planning and patient monitoring can be executed by an instance of the epistemological model described above. Of course the ontologies involved are different: there are diagnostic hypotheses, manifestations etc. in diagnostic reasoning; therapies, therapeutic problems and so on in therapy planning; alarms, critical conditions, emergency actions and so on in monitoring.

The way in which therapy planning and monitoring are other instances of the epistemological model (ST-MODEL) is illustrated in (Ramoni, Stefanelli, Magnani and Barosi, 1992; Stefanelli and Ramoni, 1992).

Epistemology, AI and cognitive psychology can be used together to develop models that explain how humans think (Thagard, 1996). I would like to illustrate the relationships and compatibility between my epistemological framework of medical reasoning and certain cognitive models of physicians’ reasoning.

As we have seen in the previous section, if abduction is considered as inference to the best explanation, abduction is epistemologically classified not only as a mechanism for selection (or for discovery), but for justification too. In the latter sense the classical meaning of abduction as inference to the best explanation (for instance in medicine, to the best diagnosis or the best therapy) is described in my epistemological model by the complete cycle abduction-deduction-induction (Josephson, Chandrasekaran, Smith and Tanner, 1986). Nevertheless, as we have seen, abduction can be considered simply as a mechanism for production of plausible hypotheses, and this is the case with my epistemological model. I think this controversial status of abduction is related to a confusion between the epistemological and cognitive levels, and to a lack of explanation as to why people sometimes deviate from normative epistemological principles. An analysis of the differences between epistemological and cognitive levels would help to clarify the issue.

From an epistemological point of view, abduction as inference to the best explanation involves the deduction-induction cycle of testing by means of multi-dimensional criteria of evaluation. Abduction as inference that provides a possible explanation of some puzzling phenomenon, is only a mechanism of discovery (or in medical diagnosis, of selection). In this latter sense abduction is the "wild hunch" that may either be a brilliant breakthrough or a dead-end: nevertheless it implies uncertainty, which can be removed or reduced only by testing the implications of selected diagnostic hypotheses against the available data. From an empirical point of view, for instance in the case of experimental research on the behavior of physicians, there is an external criterion of truth: the correctness of a diagnostic conclusion is already known (the best diagnosis) in relation to a particular condition, and this is compared to observations of a physician’s performance.

There exist many possibilities and many diagnostic performances are found: physicians make correct (best), or wrong diagnoses both by an abduction/deduction-induction cycle of testing (abduction considered as inference to the best explanation according to the complete cycle of my epistemological model), and by selective abduction (without the testing cycle).

The empirical regularities established by Patel and Groen (1991) from research on expert-novice comparisons illustrate, among other things, the role of forward reasoning and backward reasoning in medical diagnosis. Because of the revealed independence of recall phenomena from diagnostic accuracy (diagnostic accuracy is developmentally monotonic whereas recall is nonmonotonic; the development of expertise is not related to the development of increasingly better representations) the main results of this research lead to a rejection of (1) the theory of medical diagnosis as pattern recognition and (2) the theory of diagnostic expertise based on a set of production rules. Here is a résumé of certain important results from this empirical research into the various kinds of diagnostic reasoning. The first kind of reasoning - forward - as a strong problem-solving method that requires a great deal of relevant knowledge, is error-prone in the absence of adequate domain knowledge. The second - backward -, as a weak method, is used when domain knowledge is inadequate or when relevant prior knowledge is lacking. In my epistemological model, forward reasoning (knowledge-based heuristic search - Hunt, 1989), is consistent with selective abduction while backward reasoning (goal-based heuristic search - ibid.) is consistent with the deduction-induction cycle.

The research relates to the finding that, in solving routine problems in their domains, expert physicians tend to work forward from the available information to hypotheses. On the contrary, intermediate and novice physicians work from a hypothesis regarding the unknown, back to the given information. A strong relationship between diagnostic accuracy and the existence of forward reasoning has been established. (In standard experimental procedure, subjects are shown a written description of a clinical case and each subject is asked to read the clinical test for a specific period of time, after which it is removed. The subjects are asked to write down as much of the text as they can remember, and then to describe the underlying pathophysiology of the case. Finally, they are asked to provide a diagnosis - Patel and Groen, 1991; see also Groen and Patel, 1988).

All expert physicians with completely accurate diagnoses revealed the use of pure forward reasoning, followed by evaluation in order to confirm and refine the diagnosis by explaining the patient’s cues (Patel, Evans and Kaufman, 1989). When experts do not provide complete diagnoses, they use a mixture of forward and backward reasoning, that is, the generation of alternative possibilities (plausible hypotheses), followed by an evaluation phase in which the alternative diagnoses can be discriminated. The difference between accurate and inaccurate diagnoses is the presence of "loose ends". This is also the case for intermediates who do not seem to be able to filter out irrelevant information: this causes the production of loose ends, that is the activation of irrelevant searches. On the contrary, the efficacious use of pure forward reasoning expresses the idea that "a distinguishing trait of experts [...] is a knowledge of what not to do" (Patel and Groen, 1991).

In the case of doctor-patient interactive dialogues, analyzed using linguistic pragmatics methods, these authors argue that "it is expected that physicians initially adopt a data-driven strategy and later shift to a predictive reasoning strategy when they have a working hypothesis [...] the directionality of reasoning is in forward direction until some loose ends are generated, when the reasoning shifts to the backward direction to account for the loose data" (Patel and Groen, 1991). In this case also, experts arrive at accurate diagnoses because their initial hypotheses are generally accurate, which results in the accurate prediction of subsequent findings.

Forward reasoning remains associated with accurate diagnosis; during this reasoning process scientific biomedical information is not used, whereas during predictive reasoning it is used. On the contrary, residents collect a number of alternative possible diagnoses, and thereby a number of loose ends which produce diagnostic inaccuracy.

In my opinion, the cognitive concept of forward reasoning is consistent with the selective abduction of my model, because both deal with an inference from data to hypotheses. Likewise, as previously mentioned, backward reasoning is consistent with the deduction-induction cycle, because both deal with an inference from hypotheses to data. Nevertheless, in order to avoid any misunderstanding, it is necessary to illustrate various differences:

(1) epistemologically, selective abduction always implies uncertainty, although it tends to produce hypotheses that have some chance of turning out to be the best explanation; at this stage it is not known which hypothesis is the best and this type of reasoning does not possess the resources to answer the question; on the contrary, from an empirical cognitive point of view, forward reasoning characterizes an expert’s diagnostic accuracy, that is the diagnostic reasoning that is immediately successful and that establishes the best explanation. The selectivity considered as guessing plausible hypotheses is not relevant, rather forward reasoning seems to be consistent with the philosophical concept of visual abduction described above;

(2) epistemologically, the deduction-induction cycle illustrates inference to the "best" explanation involving some multi-dimensional criteria of evaluation and of the elimination of hypotheses; on the contrary, the empirical cognitive results show that this kind of reasoning is typical of intermediates’ diagnostic "inaccuracy" - although they recall better than experts and novices - because of the effect of the failure of forward reasoning, and of the consequent production of unnecessary searches (clearly judged "unnecessary" post hoc).

It is interesting that conventional curricula (CC) (where basic science courses are taught before the clinical training) and problem-based learning curricula (PBL) (where basic science is taught in the context of clinical problems and general heuristics are specifically taught) lead students, when they generate explanations, to develop respectively abductions (forward reasoning) or to perform the whole abduction-deduction-induction cycle using relevant biomedical information (backward reasoning). The results of this cognitive research can be found in Patel, Groen and Norman, 1993; see also Patel, Evans and Groen, 1989b.

Educational transfer occurs when knowledge acquired in a specific context or for one purpose is used in a different context or for a different purpose. Many kinds of transfer involve the application of knowledge in performing basic cognitive tasks; these tasks are performed in conditions that differ from those under which the pertinent knowledge was acquired.

Following Patel et al. (Patel, Evans and Groen, 1989a) we can affirm that clinical medicine and the biomedical sciences constitute two distinct and not entirely compatible worlds, with separate ways of reasoning and quite different ways of structuring knowledge. Clinical knowledge is based on a complex taxonomy which relates diseases and symptoms to the underlying pathology. On the contrary, the biomedical sciences are based on very general principles which define chains of causal mechanisms. Thus, learning how a set of symptoms relate to a diagnosis may be very different from learning what causes an illness. In both cases learning is not only epistemologically but also situationally directed. Clinical training is built on situations involving hospital and patients. The biomedical sciences are built on laboratory situations. However, the latter are learnt primarily from textbooks whereas the former is based on living encounters. Clinical training is highly situated, whereas biomedical instruction is highly unsituated. Hence, it may be better to teach basic science separately, so that the appropriate knowledge can be activated in spontaneous problem-solving situations.

The persistence of scientific errors in PBL students’ explanations indicates that there are serious problems as regards the specific implementation of the PBL curriculum. If students merely gather experiential knowledge, they run the risk of building a huge situation-knowledge base that is not connected with theoretical, general biomedical knowledge. Moreover, it is well-known that the deliberate use of a rigid epistemological mechanism may also interfere with the proper richness of an expert’s forward reasoning.

The PBL curriculum specifically involves the specific teaching of hypothetico - deductive reasoning, that is, the reasoning illustrated by the whole cycle of my epistemological model. Consequently, students learn a systematic process of thinking - yet do they end up with an epistemological awareness of the main methodological topics involved?

In these students it has been empirically noted the systematic use of clinical information and the tendency to elaborate extensively. Nevertheless, in CC students we have not observed a systematic mode of reasoning: the mode of reasoning most often employed is "forward", together with the tendency to explain cases on the basis of a single diagnosis rather than an extensive list of differential diagnoses. It has been also observed a lack of scientific background in many PBL students; on the contrary, the surprising degree of structure used by CC students in their explanations may be due to their superior scientific background. The active application of knowledge (Basic Science) in understanding clinical texts and reasoning about problems will lead students to construct stronger relations between concepts, increased coherence of knowledge networks and an increased number of interrelations between concepts. In addition, a process of knowledge restructuring takes place.

Boshuizen and Schmidt (1992) have shown that the repeated application of biomedical knowledge in clinical reasoning at the earlier stages of expertise development leads to the subsumption of lower-level detailed propositions under higher-level, often clinical, propositions. This is the well-known phenomenon they termed encapsulation: the result consists of easily accessible and flexible knowledge structures with very short research paths. Biomedical knowledge plays a tacit role since it is encapsulated in clinical knowledge. Our findings suggest a tacit role for biomedical knowledge in expert clinical reasoning. This tacit role contradict the conviction (Patel, Evans and Groen, 1989a) that biomedical and clinical knowledge essentially represent two different worlds.

Empirical results have in turn shown that an expert facing familiar problems applies increasingly less detailed medical knowledge than experts facing unfamiliar ones. Knowledge applied in such routine cases has already become encapsulated. Furthermore results from Boshuizen and Schmidt state that the detailed biomedical knowledge encapsulated under high-order propositions remains available and can be retrieved whenever necessary, for instance in explanations and communications. For example, when asked to explain the direct connection to be made between drug abuse and endocarditis Patel and Groen’s subjects would probably easily expand that higher order proposition to a chain of at least five other propositions.

Boshuizen and Schmidt’s model is complicated by the notion of illness script (Feltovich and Barrow, 1984). The illness script is caused by that process that takes place when a student acquires proficiency in clinical reasoning, when he or she is exposed to real patient. All illness scripts are assumed to develop as a result of extended practical experience: illness scripts will tend to become richer, more refined, and better turned to practice, while causal, biomedical knowledge which they incorporate becomes further encapsulated as a function of the amount of actual experience with a certain (class of) disease(s).

Also at the computational level (cf. ABEL’s knowledge multilevel link-structure - Patil, 1981 - and NEOANEMIA - Lanzola, Stefanelli, Barosi and Magnani, 1990) we find the possibility of having intermediate explanations that show the reasoning of the performed inference.

Since the mid-’70s there has been widespread agreement among AI scientists that models of a problem solving agent in a KBS should incorporate knowledge about the world (ontological commitment) and some sort of an abstract procedure (inferential commitment) for interpreting this knowledge in order to construct plans and take action.

There are many AI ways of exploiting basic science resources in ontological levels involved in the deduction-induction cycle of second generation medical KBSs. This is the case with NEOAMEMIA but also with earlier medical KBSs, such as CASNET, CADUCEUS (Pople, 1985) and ABEL (Patil, 1981). The KBS ontology that adequately and "deeply" represents knowledge, as it is organized in scientific medical theories (causal or taxonomic) (Kuipers, 1987; Milne, 1987; Simon, 1985), goes beyond first generation "shallow" KBSs that only mapped knowledge into pragmatic constructs derived from human experts - in the latter case the ontology was compiled in conjunction with the inference procedure, thereby becoming implicit - (Chandrasekaran and Mittal, 1982). In this sense the new architectures combine a more principled knowledge of the domain with the simple heuristic knowledge that was the main type of knowledge exploited in first generation KBSs. While in the hypothesis generation phase NEOANEMIA exploits compiled heuristic pathways specified by an expert, a separate and explicit representation of causal and taxonomic ontology is used in the deduction- induction phase. They have been represented using a simple two layer network (i.e. representing what clinical evidence may be expected for each disease) and QSIM (Kuipers, 1986 and 1987; Ironi, Stefanelli and Lanzola, 1992) for representing available knowledge on pathophysiological system dynamics.

If the pure philosophical and cognitive task is to state correct rules of reasoning in an objective way, the use of computer modeling may be a rare tool in these investigations because of its rational correctness. This cooperation should prove very fruitful from an educational perspective too: reciprocally clarifying both philosophical/cognitive and AI theories of reasoning will provide new and very interesting didactic tools.

I have tried to clarify the role of problem solving in medical education, as different from conventional basic science-centred education. Although an epistemological and logical awareness of the main methodological topics concerning medical science and reasoning would be important from a general theoretical point of view, it does not improve the physician’s performance. Anyway, does the PBL curriculum actually involve the specific teaching of hypothetico-deductive reasoning, that is the reasoning illustrated by the whole cycle of my epistemological model? Do these students end up with an epistemological awareness of the main methodological topics involved?

The empirical results show that backward reasoning is typically of intermediates’ diagnostic "inaccuracy" - although they recall better than experts and novices - because of the effect of the failure of forward reasoning, and of the consequent production of unnecessary searches. Moreover, the persistence of scientific errors in PBL students’ explanations indicates that there are serious problems as regards the specific implementation of the PBL curriculum. If students merely gather experiential knowledge, they run the risk of building a huge situation-knowledge base that is not connected with theoretical, general biomedical knowledge: there is a lack of scientific background.

According to Boshuizen and Schmidt biomedical knowledge plays a tacit role since it is encapsulated in clinical knowledge as a function of the amount of actual experience with a certain class of diseases. This tacit role would contradict Patel and Groen’s conviction that biomedical and clinical knowledge essentially represent two different world. I think that the concept of encapsulation should not involve this consequence. The result of encapsulation, that is, easily accessible and flexible knowledge structures with very short research paths, leads to an epistemologically different kind of knowledge.

I have unified medical reasoning by the notion of selective abduction: this kind of reasoning explains and executes the three generic tasks of diagnosis, therapy planning, and monitoring, correctly establishing the level of evaluation procedures and ontological medical complexity.

The relevance of abduction ensures it a prominent role in methodological aspects of medical education and practice. Moreover I have tried to show that the idea of abductive reasoning might be a flexible epistemological interface between other related notions (induction and deduction, best explanation, perception, forward and backward reasoning, defeasibility, discovery, and so on) all of which are involved in medical reasoning and in medical education but, at the same time, are of great theoretical interest in general.