This paper presents work in progress on artificial intelligence in medicine (AIM) within the larger context of cognitive science. It introduces and develops the notion ofemergence both as an inevitable evolution of artificial intelligence towards machine learning programs and as the result of a synergistic co-operation between the physician and the computer. From this perspective, the emergence of knowledge takes placein fine in the expert's mind and is enhanced both by computerised strategies of induction and deduction, and by software abilities (...) to dialogue, co-operate and function as a cognitive extension of the physician's intellectual capabilities. The proposed methodology gives the expert a prominent role which consists, first, of faithfully enunciating the descriptive features of his medical knowledge, thus giving the computer a precise description of his own perception of basic medicine, and secondly, of painstakingly gathering patients into computerised case bases which simulate exhaustive long-term memory. The AI capacities for knowledge elaboration are then triggered, giving rise to mathematically optimal diagnoses, prognoses, or treatment protocols which the physician may then evaluate, accept, reject, or adapt at his convenience, and finally append to a knowledge base. The idea of emergence embraces many issues concerning the purpose and intent of artificial intelligence in medical practice. Particularly, we address the representation problem as it is raised by classical decisional knowledge-based systems, and develop the notions of classifiers and hybrid systems as possible answers to this problem. Finally, since the whole methodology touches the problem of technological investment in health care, now inherent in modern medical practice, some ethical considerations accompany the exposé. (shrink)

Two approaches to the study of diagnostic thinking are compared, one mainly propositional, namely that of Patel and Groen (1986), the other mainly semantic, that of Lemieux and Bordage (1986). Patel and Groen analyzed the linear dimension of cardiologists' discourses while solving a case of acute bacterial endocarditis, that is, the before and after propositional rules. A secondary analysis of two of their pothophysiological protocols is done using structural semantic techniques from Lemieux and Bordage where the vertical dimension of the (...) discourses is analyzed, that is, the levels of meaning or semantic axes. Contrary to Patel and Groen's position, making an accurate diagnosis is not explained in terms of pure forward reasoning through networks of causal rules, but by means of networks of semantic qualities abstracted from the symptoms and signs. The semantic qualities operate in the clinician's mind in terms of binary oppositions (e.g., sudden-gradual, unilateral-bilateral) whereby each pair of properties constitutes a semantic axis (e.g., sudden-gradual). The successful diagnosticians are those who use the most diversified and pertinent set of semantic axes and, therefore, have a deeper representation of the problem. (shrink)

Expert chess players, specialized in different openings, recalled positions and solved problems within and outside their area of specialization. While their general expertise was at a similar level, players performed better with stimuli from their area of specialization. The effect of specialization on both recall and problem solving was strong enough to override general expertise—players remembering positions and solving problems from their area of specialization performed at around the level of players 1 standard deviation (SD) above them in general skill. (...) Their problem‐solving strategy also changed depending on whether the problem was within their area of specialization. When it was, they searched more in depth and less in breadth; with problems outside their area of specialization, the reverse. The knowledge that comes from familiarity with a problem area is more important than general purpose strategies in determining how an expert will tackle it. These results demonstrate the link in experts between problem solving and memory of specific experiences and indicate that the search for context‐independent general purpose problem‐solving strategies to teach to future experts is unlikely to be successful. (shrink)

Effective person-centred care requires recognition of the personhood not only of patients but of practitioners. This chapter explores the consequences of this recognition for major debates in medical epistemology, regarding clinical reasoning and the relationship between research and practice. For too long these debates have been dominated by false dichotomies - subjectivity versus objectivity, judgement versus evidence, reason versus emotion. Based on flawed understandings of such core concepts as “objectivity” and “engagement”, this distorted dissection of the subject-object relationship has served (...) to depersonalise practice. The costs of this depersonalisation include over-regulation and micromanagement of healthcare processes by administrators and payers at the same time that information from clinical research remains under-utilized and the personhood of patients risks being ignored. Science is a human practice, founded in a broader conception of human reasoning, ontologically dependent on human beings living and engaging with the world in social, emotional and ethical contexts. After looking at different conceptions of epistemic hierarchies and their uses in the analysis and evaluation of reasoning in a range of practice contexts, we propose a “nested hierarchy” that effectively turns upside-down the flawed evidence hierarchies that have helped to depersonalise care. T.S. Eliot’s “wisdom, knowledge, information” scheme (to which we add “data” below “information”) provides a model for a person-centred epistemic hierarchy. This crucial, person-centred inversion represents levels of awareness that characterise more or less developed thinking and judgement on the part of the particular practitioner. (shrink)

I examined the proposition that there are psychological limits on what scientific problems can be solved, and that these limits may be based on a failure to be able to produce imagable, observation-based models for any possible solution, a position suggested by philosopher Colin McGinn in an argument attempting to prove that the mind-body problem is unsolvable. I examined another likely candidate for an unsolvable problem -- the ultimate origin of the universe (i.e., what might have preceded the Big Bang (...) or any other starting point; why there should be something rather than nothing) -- by exploring the reasoning of physicists about this problem and measuring visual imagery frequency and vividness, with the expectation that those who most believed the problem unsolvable would be more frequent/vivid imagers and therefore more affected by the apparent impossibility of producing an imagable solution. Eight physicists were interviewed and imagery frequency and vividness measurements performed using Cohen & Saslona's IDQ-IHS and Marks's VVIQ, respectively. All subjects considered the problem unsolvable within today's physics and all but one thought the problem still meaningful, though none were optimistic about a solution. The one subject who dismissed the problem had the lowest imagery frequency score, and there was also a significant rank order correlation (r = 0.83, p < .02) between degree of belief in problem unsolvability (extended to include viewing the problem as meaningful and not already solved) and a composite imagery frequency/vividness score, though the sample was too small to control for some possible confounding factors. (shrink)