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% Peter Szolovits
% Using Artificial Intelligence Models of Medical Decision Making in
%	Medical Education
% format: TeX
% reference: 

%@InProceedings{Szolovits83,
%   Author="Szolovits, P.",
%   Title="Using Artificial Intelligence Models of Medical Decision
%                Making in Medical Education",
%   BookTitle="Meeting the Challenge: Informatics and Medical Education",
%   Editor="Pages, J.-C. and Levy, A. H. and Gremy, F. and Anderson, J.",
%   Publisher="North Holland",
%   Year=1983,
%   Pages="271-281"}


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\title{AI Models of Medical Decision-Making}
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{\large\bf Using Artificial Intelligence Models

of Medical Decision Making

in Medical Education}


Peter Szolovits
MIT Laboratory for Computer Science
Cambridge, Massachusetts}

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{\center\cp
This is the article accompanying a presentation at the 1983 IMIA
Conference on Computers and Education, Chamonix, France, March 1983.  It
is to appear in a book based on the proceedings of that conference, to
be published by North Holland Publishing Company.

}

\vfil
{\center\cp
This research was supported (in part) by the National Institutes
of Health Grant No.\ 1 P01 LM 03374-04 from the National
Library of Medicine.

}
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\vbox to 2.3in{\vfil
\ctrline{USING ARTIFICIAL INTELLIGENCE MODELS OF MEDICAL DECISION MAKING}
\ctrline{IN MEDICAL EDUCATION}
\vbox to 1.5in{\vfil
\ctrline{Peter Szolovits}
\vskip 12pt
\ctrline{Laboratory for Computer Science}
\ctrline{Massachusetts Institute of Technology}
\ctrline{Cambridge, Massachusetts}
\ctrline{U.S.A.}
\vfil}}

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Medical education involves the teaching of not only basic medical
sciences but also clinical proficiency.  Although traditional training
programs teach this by apprenticeship and experience, formal theories of
how expert clinicians reason and make decisions can guide the medical
student in acquiring the ``art'' of medicine.  Research in the
application of artificial intelligence methods to clinical
decision-making problems has identified just such formal theories and
begun to test them in consultation programs that simulate expert human
performance.  The use of (a) ``trigger'' findings to suggest diagnostic
hypotheses to be explored, (b) differential diagnosis as a guide to
exploring the space of possible etiologies, (c) reasoning across
multiple levels of detail to help focus and refine the diagnostic
process, and (d) expectations that drive the interpretation of
therapeutic results are all mechanisms now employed in various programs
that appear also to underlie expert human medical reasoning.  We have
recently begun to teach such explicit theories to small numbers of
medical students at Harvard and Tufts Medical Schools.  If these
programs themselves become practically usable tools in the practice of
medicine, their introduction will require dramatic changes in the
teaching of medicine.

}


\Section Introduction:  AI in Medicine

\pp Since the earliest days of the advent of the modern electronic
computer, researchers have been fascinated by the prospect of getting
such machines to ``perform intelligently'' at tasks thought to require
human intelligence.  Although much of the early work focused on
game-playing programs, progress in the methods of {\sl artificial
intelligence} (AI) has permitted within the past decade the creation of
a handful of experimental computer programs that, at least
experimentally, appear capable of giving expert-like medical
consultations [\ref \Yub, \ref \Longb, \ref \Kulia].  What is likely to be the impact
of these methods and the descendants of these early programs on the
education (and indeed the practice) of future generations of physicians?
In this short paper I will argue that there are at least two principal
effects we can plan for.  First, the scientific principles of the {\sl
practice} of medicine (as they are uncovered in the attempt to build
expert medical programs) can and should be taught to today's medical
students, to provide them a clearer understanding of what clinical
expertise is and (perhaps) how it may be acquired.  Second, because
computers appear likely to play an ever-increasing role in medicine (as
in other areas of our society), we should assure that medical students
come to know not only how to live with but also to help {\sl create} the
programs that are likely to form such an important part of their
professional lives.  In a more speculative vein, we should also consider
the possible redefinition of the role of the human physician as some of
his or her clinical and intellectual function is
{\crlist
\aitem coupled to computer-based record-keeping and consultative advice,

\aitem overseen by diagnostic and therapeutic programs that monitor for
and warn of possible errors, or

\aitem directly replaced by a computer program or by other specialists
aided by computers.
 
}


\Section Motivations for AI in Medicine

Although I will not attempt to review the state of the art in the
applications of AI to Medicine
\foot{I have recently edited a volume of reports on some of the most
interesting recent programs and current research directions [\ref \Szolg].}
(a field called AIM), it is worth considering why both computer
scientists and physicians have been and continue to be attracted to this
research endeavor.

First is the obvious possibility that computer programs, charged with
the knowledge of leading clinical specialists, can serve as consultants
and error monitors to clinicians and, perhaps, other health-care
providers, and make the provision of medical services better, more
widespread, more reliable, and perhaps less costly.  This possibility
rests on the observation that more highly-trained and experienced
physicians (experts) can do better at diagnostic and therapeutic tasks
than their less-trained or less-experienced colleagues.  If the
knowledge and reasoning that accounts for this advantage can be captured
in formal theories and encoded in computer programs, then it will become
possible to make high-quality consultations available via the program to
virtually any practitioner who is in doubt about some aspect of a
current patient's case, without the cost, inconvenience and potential
embarrassment associated with seeking a consult from a human expert.  In
addition, the unavailability of experts in many parts of the world and
their maldistribution in even the best-served countries argues for the
desirability of such computer-provided expert advice.

Even in locations well-endowed with excellent medical staff and
consultants, programs may be extremely useful in a humble role designed
simply to avert errors of human oversight.  If patients' records are
routinely available in computer-readable form, a program perhaps designed to
give consultations could be run in a ``background'' mode not intended to
give advice but simply to verify that recorded data about the patient
are consistent with the proposed diagnostic hypotheses and therapeutic
plans.  In this mode, even moderate disagreements between the human
physician's recorded intentions and the program's analysis need not
trigger alarms, but if the program is unable to reconcile these plans
with some plausible interpretation of the data, it should then bring the
discrepancy to the physician's attention.  Often, of course, it will
turn out that the physician simply knows some facts about the patient
that the program is not aware of (the medical record is incomplete), but
occasionally the discrepancy will be real and will reflect a fact
overlooked by a tired physician or a bit of medical knowledge not
adequately taken into account.  Serious errors can be caught in this
way, and physician acceptance of such a monitoring system can be very
good if the system is designed appropriately [\ref \Morra].

The second attraction to AIM, especially for computer scientists, is the
large number of difficult technical challenges posed by medical expert
programs to the tools of artificial intelligence research.  As AI has
graduated from the oversimplified domains of its youth---the blocks
world and various game-playing programs---the challenges of representing
medical knowledge in its full richness and the problems of capturing the
reasoning methods and style of the artful practitioner have formed the
basis for much good AI research.  The particular necessity for AIM
programs to be able to explain their conclusions in order to have much
chance of clinical acceptance, for example, has led to a strong effort
in explanation generation within the AIM community.  The need to deal
with uncertainties as an integral part of the medical decision process
has led to many new ideas on how to represent and compute with such
uncertainties.  Indeed, much of the technology of ``expert systems,''
now being developed in application fields such as oil exploration,
banking services, electronics design, etc., has come directly from
successful AIM prototypes.

The third characteristic of AIM research, its promise to provide useful
models of the process of clinical reasoning and action, has been most
attractive to the physicians collaborating in our group.  These
researchers are chiefly academic doctors, splitting their time between
patient care, research, and the education of medical students.  One of
them once described the process of medical education as two years of
force-fed basic science followed by two years of being wheeled around
the wards in a carriage, with the expectation that by osmosis the good
clinical practice of medicine would seep into the minds of the students.
Many are frustrated by their inability to be nearly as precise about
questions of clinical practice as they can be about questions of
physiological process.  The theories we have developed and included in
our programs provide at least one good framework around which to
organize an understanding of clinical practice.

Each of the three motivations I have discussed is significant for the
future of medical education.  In the following sections I will describe
some of the models of clinical reasoning that I believe are important, I
will discuss the need for medical students to come to know something
about the techniques of computer science and artificial intelligence,
and I will look at what we may expect from the future adoption of AIM
programs into medical practice.


\Section The Physician as Scientist

The most interesting conclusion we have reached in studying medical
reasoning is that, in diagnostic thinking, {\sl physicians behave as
scientists!\/}\foot{This conclusion is surprising only because so much has
been made of the ``art'' of medicine and because the public considers
medical knowledge to be in some way mysterious and ineffable.  Much that
happens in medicine is, of course, deserving of wonder and artistic, but
nevertheless physicians apply a variant of the scientific method in
their diagnostic reasoning.}  In particular, the diagnostic process may
be viewed as the following:
{\crlist
\aitem From some initial set of data (such as the presenting
complaints), the physician formulates an hypothesis of what ails the
patient.

\aitem The physician focuses attention on some particular aspect of the
hypothesis, one that needs further refinement or definition, or one that
is critical to further decisions.

\aitem A question or set of questions is generated that is meant to help
confirm or disconfirm the current hypothesis, and perhaps to provide
information to help refine it.  This question may be literally a
question to the patient or an examination or request for a laboratory test.

\aitem Newly-provided data (either in answer to the question or
incidentally provided) are analyzed for their consistency with the
hypothesis.

\aitem The hypothesis is either accepted as confirmed or reformulated in
light of the new evidence, and this reasoning process is resumed with
step ii.

}

Studies both by our group [\ref \Kassa] and Elstein's [\ref \Elsta] (who call
it ``hypothetico-deductive reasoning'') support this
view, and we have successfully adopted it---with great variations in
detail---in numerous diagnostic programs.

\Subsection Triggering

The ``physician as scientist'' theory suggests that rather specific
hypotheses are formed by a physician based on very limited information
about the actual condition of the patient.  Thus, somewhat surprisingly,
we often see the physician considering individual diseases as diagnostic
hypotheses in preference to broad categories of disease, even though the
partial evidence is quite insufficient to establish the specific
hypothesis.  Our interpretation of this phenomenon is that, cognitively,
it is much more useful to have a specific disorder under consideration
than some broad category, because the specific disorder naturally
suggests specific data that should be gathered to help confirm or
disconfirm it.

Some mechanism is therefore needed to suggest appropriate hypotheses
based on the few data that are available early in diagnosis.  We have
proposed a mechanism called ``triggering'' to fill this role.  Consider
first the case of the pathognomonic finding:  If a datum is so strongly
associated with a disorder that its presence is sufficient to confirm
the disease hypothesis, then obviously its presence must (at least)
evoke that hypothesis for consideration.  Unfortunately, the number of
truly pathognomonic findings is small, and few diagnostic conclusions
can be drawn based on the presence of a single finding.
This idea is, however, useful in controlling the {\sl consideration}
rather than the {\sl confirmation} of diagnostic hypotheses.

The Present Illness Program ({\sc pip}), for example, uses an
organization of medical knowledge in which each hypothesis lists its
expected findings, and in which those findings that are truly
pathognomonic or highly evocative are indicated as ``trigger findings''
of the hypothesis [\ref \Paukb].  When one of these is reported to
the program, it immediately activates the corresponding hypothesis.
This mechanism provides the program with the ability to respond rapidly
to suggestive evidence, to use a single early datum as the evoker of a
very specific diagnostic hypothesis.

This feature of the program's organization suggests that a useful form
of medical knowledge is an inversion of the typical form taught in
medical schools and used as an organizing principle in medical texts.
The relationships between diseases and their manifestations must not
only be indexed by disease but also by manifestation, so that at least
from the more specific and indicative findings one can immediately reach
the hypotheses that should be considered to account for it.  Although I
would certainly not advocate displacing the teaching of diseases and
their common patterns by memorization of what disorders each symptom is
indicative of, there is a role for the latter in hypothesis formation
just as there is a role for the former in the validation or rejection of
the hypothesis
This latter
structure supports the observed tendency of clinical experts to
``guess'' a specific disorder from inadequate information.  It may be one
of those information structures and strategies internalized by the
medical students during those ``osmotic'' years of learning on the
wards.

\Subsection Differential Diagnosis

If the expert clinician makes unfounded guesses to suggest a working
hypothesis around which to formulate his or her further diagnostic
reasoning and questioning, then that process will certainly sometimes
lead to error.  It is vital, therefore, that the physician can
effectively and efficiently recover from such an error.

In computer programs it is common to use a style of control known as
{\sl backtracking} to support the making of guesses and their retraction
when found inappropriate.  The problem facing a program that must make a
succession of choices in its operation can be visualized as a tree,
where each choice corresponds to the sprouting of one branch for each of
the possible choices.  The program can ``climb'' only one branch at a
time, but may have to return to try a different branch if the first
guess was incorrect.  In a program using such a mechanism, whenever a
choice point in encountered, one of the alternatives is selected and a
record is made of this choice.  If the program later discovers an
inconsistency due to this choice, it can restart its calculations from
the point of the original choice but selecting one of the other
alternatives.  If no more untried alternatives exist and all those tried
have led to failure, then the need to make the choice itself is
invalidated and any previous guesses that have led to this situation are
abandoned in favor of {\sl their} alternatives.  This is a simple,
though unattractive form of hypothesis-based reasoning, for which we see
little evidence in the behavior of expert clinicians.  Backtracking
does, interestingly, appear to play an important role in human
problem-solving skill involving problems where the subject is not an
expert [\ref \Neweb].  Indeed, we have seen some evidence that
non-expert physicians (and especially inexperienced medical students)
completely abandon a hypothesis under consideration when that hypothesis
is seen to conflict with new data, and such people are then left
ineffectively searching for a reasonable working hypothesis.

A key is needed for escaping from this unwanted consequence of a
willingness to guess early.  For the expert physician, it appears that
the exact manner in which an existing hypothesis fails to be supported
by the data gives a very strong clue to just what other hypothesis ought
to be considered instead.  Rather than backing up to make a new initial
guess, then, the expert uses knowledge of how the old guess failed to
move directly forward to a new, improved guess.  Sections of medical
texts discussing differential diagnosis often provide just the sort of
information needed here:  that the disease under discussion can be
confused with certain others and that some small set of findings can be
used to help suggest whether one of those is a more appropriate
hypothesis for consideration.

Our experience analyzing physicians' cognitive behavior suggests that
employing a highly-developed set of such differential indicators is one
key component of expertise in medicine, leading to more efficient
diagnostic problem-solving than the backtracking approach.  Although
other investigators have not uncovered significant differences between
the diagnostic styles of expert and novice diagnosticians, we believe
that this knowledge of differential criteria and relationships is a
promising place to look for such differences.

In this section I have briefly described two ideas---triggering for
hypothesis formation and differential diagnosis for hypotheses
revision---that appear to support the expert physician's use of
hypothetico-deductive reasoning as the basis of diagnosis.


\Section Reasoning with Multi-Level Models

It is a truism that simple cases should be diagnosed simply, whereas for
complex cases we may reasonably expect that the process of diagnosis
will itself be complex.  According to the diagnostic model presented in
the previous section, the simplest case is that in which the physician
forms the correct initial hypothesis and then finds that each new fact
uncovered exactly supports the expectations of the initial hypothesis.
What happens, however, if the facts turn out to be inconsistent with the
initial hypothesis and yet no rules of differential diagnosis apply to
suggest a clearly superior alternative hypothesis?

The traditional answer to the problem of discrepancy in computer-based
diagnostic programs is that some numerical ``measure of belief'' is used
to represent the program's certainty in each hypothesis, and in the face
of inconsistent answers the belief in that hypothesis is lowered.  These
numerical measures of belief may be based on actuarially-collected
likelihoods, on subjective probability estimates, or on a variety of
pseudo-probabilistic numerical schema.  The key element of each of these
approaches is that competing hypotheses are compared according to such a
common numerical measure, and that the ``score'' of each is computed
based on a match between its typical presentation and the data observed
about the particular patient at hand.  Typically, a highly-scoring
hypothesis will replace a lower-scoring one as the program's leading
contender; thus the ultimate effect of discrepant evidence is to lower a
hypothesis' score until it falls behind a better-supported competitor.

This method of aggregating the effects of evidence that seems partly in
favor of and partly against some hypothesis is not an attractive model
to teach medical students because of well-known difficulties that people
encounter in making and combining quantitative estimates of likelihood.
In addition, it appears inefficient and unwarranted even for computer
programs.  It often seems the case that such conflicts are not simply
nuisances to be gotten around but indeed are the keys to discover the
true complexity of a diagnostic case.  The unexplained extra finding in
the context of a clear-cut disease may in fact be the first clue of a
simultaneous second disorder.  The unexpectedly-normal serum potassium
level may indicate that partial treatment was already begun.  How can a
program's medical knowledge and reasoning be organized in such a way
that such unsuspected complexities become identified and pursued when
appropriate?


\Alto_figure {A multi-level description of the relationships in a case among
salmonellosis, acidosis, vomiting and hypokalemia (from [\ref \Patic]).  Concepts
and their interconnections in the top levels of the diagram correspond
to clinical associations; at the bottom level they represent
pathophysiological knowledge.  The ABEL program uses such a model to
reason about complex cases of acid/base and electrolyte disorders.}
{8.77in} {pages1}			% include full-page Alto figure

\pp
In our recent work on the diagnosis of acid/base and electrolyte
disorders we have introduced a representational mechanism that supports
the examination of a case to varying levels of detail, guided by the
appearance of apparent inconsistencies that force a move to a more
detailed level [\ref \Patib, \ref \Patic].  At the top levels of the
representation aggregate clinical and associational relationships are
used.  For example, we may express here the fact that severe diarrhea of
several days' durations is very likely to cause metabolic acidosis,
dehydration, moderate hypokalemia, etc.  Each concept and each
relationship at this level can be expanded into a small network of
concepts and relationships at the next more detailed level, and our
current program spans five levels of which the most detailed represents
medical knowledge in terms of pathophysiological concepts.  Thus, the
simple predictive relationship between diarrhea and acidosis may be
expanded until its relationships to the homeostatic mechanisms of
respiratory and renal regulation become apparent.  Often a discrepant
phenomenon at the less detailed (more aggregate) levels can be explained
successfully by looking in greater detail at the underlying machinery
that predicted the contradicted more aggregate hypothesis.  A sample of
such a representation structure from our program is shown in Figure 1.

We value the cognitive skill of being able to elaborate any relationship
that enters into the analysis of a case into a more detailed structure
of the mechanisms that ordinarily establish that relationship.  This is
what permits the expert clinician to solve the most complex problems and
to understand truly novel situations.  It permits the creation of new hypotheses that
will simultaneously account for all of the apparently discrepant data in
a difficult case.  This skill is already taught, of course, especially
during discussions at clinical rounds; our use of it in computer models
of diagnosis strengthens my belief in its importance to the medical
student.


\Section The Patient-Specific Model in Therapy

The processes of diagnostic and therapeutic decision-making are ideally
highly intertwined.  My view is that rather than each being a single
effort, they form a continuous sequence of reiterated steps.  Thus,
trying some particular therapy can be a form of diagnostic
investigation; at the least, if it is successful it will yield a strong
confirmation that the diagnostic hypothesis was correct.  Further, the
effects of therapy must typically be ``diagnosed'' to determine whether
its effects were those expected and to adjust future therapy based on
the patient's past experience.  It is central to this view that our
programs must maintain a coherent account of what they know and expect
about the patient; this is implemented in a data structure we have
called the {\sl patient-specific model} (PSM).


\Alto_figure {The Digitalis Therapy Advisor program performs therapeutic
reasoning that leads to frequent re-evaluations of the patient's
condition and revisions in the therapy plan.  The program maintains a
patient-specific model that records its knowledge about the individual
patient, and at each session bases its judgments on a comparison of its
expectations with what has actually occurred.}
{4in} {pages2} 

\pp
In a series of programs we have built for advising on therapy with the
drug digitalis [\ref \Gorrd, \ref \Szoli], the establishment and maintenance of the
PSM plays a central role.  Digitalis is used in the management of
patients with congestive heart failure and certain arrhythmias.  It is a
potent drug whose use carries significant risks of toxicity, and this
danger is exacerbated by rather large inter-patient variations in the
drug levels that lead to both therapeutic responses and toxic reactions.
The Digitalis Advisor's method of prescribing the drug, assessing the
patient's response and revising the therapeutic plan is shown
schematically in Figure 2.  The reason digitalis is to be given, the
patient's age, weight, height and current disease state, and the history
of his or her response to this drug determine the initial goal for the
serum digitalis level.  Abnormal factors that may predispose this
patient to digitoxicity make the program more conservative in its use of
the drug, typically reducing (at least temporarily) its goal drug level.
A pharmacokinetic model is then used to calculate a practical
approximation to an ideal dosage schedule, and the program makes its
recommendations to the user.\foot{This program has not been used
clinically, but only in retrospective clinical trials.}

After some time has passed and the patient has (presumably) been
treated, the program is run again to assess the effectiveness of
therapy.  Note that its behavior at this time is very different from the
first time it encountered this patient, because by now it has stored a
PSM that records whatever the program learned about the patient in the
past, its previously-recommended therapy, and its expectations about how
the patient's condition should have changed in response to the
recommended therapy.  The program first finds out what therapy was
actually administered (nothing requires the user to follow the program's
recommendations), and then by comparing its expectations with what is
reported about the patient it forms a judgment about whether the drug
thus far given has been therapeutically {\sl ineffective}, {\sl
partially effective} or {\sl fully effective}, as well as whether there
is {\sl no indication of digitoxicity}, {\sl suspected toxicity} or {\sl
definite toxicity}.  For each of the nine possible aggregate situations
(e.g., no signs of toxicity and partially effective therapy), the program
contains rules to revise its therapeutic plan, principally by altering
the goal drug level.  This new goal level is then used as before
(adjusted for anticipated toxicity, etc.) as input to the
pharmacokinetic model to compute a new treatment recommendation.

The fundamental idea, that therapy is based on a continuing
re-evaluation of what has happened in comparison to what was expected,
is key to understanding the clinician's approach to therapeutic
reasoning.  This idea is captured at the heart of our Digitalis Advisor
program, and forms another specific lesson we can teach to medical
students.  In fact, this program has served as our testbed for the
development of computer-generated explanation capabilities [\ref \Swarc],
which make it possible for the program itself to explain its models and
reasoning, and to justify its recommendations directly to the user.

\yyskip 
In the past three sections I have described three sets of ideas around
which we organize the structure and behavior of our medical consulting
programs.  Each of these evolved from our studies of the actual clinical
behavior of expert physicians and each has been refined as we have
worked to match the performance of the program to that of the experts on
whom it is modeled.  To the extent that we have successfully captured
some insights into the process of medical reasoning, these models used
in our programs form teachable theories of the practice of medicine.  We
have, therefore, begun to teach groups of medical students about our
efforts in building computer-based medical expert systems and about our
views on medical practice that underlie these programs.  We are not able
to claim that our efforts are turning out better physicians---a
difficult judgment to make in any case---but we have been rewarded by
highly interested and pleased students and by the enthusiasm of our
medical colleagues.


\Section The Impact of AIM Research on the Future of Medical Education

In most of this paper I have argued the value of explicitly teaching the formal
models of medical decision-making that we uncover (or invent) as we seek
to develop computer programs that can act as sophisticated medical
consultants or error monitors.  Two other issues are worth examining
briefly, as they will be critically important in the future of medical
education.

First, the coming inevitable ubiquity of rather high-powered computer
systems means that large numbers of people in every profession
will begin to try their hands at creating sophisticated programs to
automate part of their professional tasks.  In medicine we have already
begun to see the proliferation of rather simplistic ``home-made''
programs purporting to be keepers of medical records, memory aids,
diagnostic consultants, and error detectors for straightforward medical
cases.  Although such a tool used by its builder can be a source of
pleasure, pride and valuable help, there are serious potential problems
if such programs, not based on well-established and accepted principles,
become distributed for use by those unaware of their assumptions or
limitations.  Rather than discouraging the creation of such
individually-inspired products, however, we should strive to provide the
educational background that will enable future generations of physicians
to build such tools with a far better understanding of the problems they
face and the techniques that are already available as a basis for the
creation of new, imaginative programs.

Second, if the attempt to capture much of medical expertise in
consulting programs succeeds, we may anticipate dramatic changes to the
demands placed on physicians and therefore to the educational process
that is to prepare them.  As Schwartz wrote in a seminal 1970
examination of the prospects for computer use in medicine [\ref \Schwa],
{\quote
``$\ldotss$ students and physicians exposed to prototype computer-based
consulting programs commonly express anxiety and displeasure at the
prospect of practicing medicine within a system that has as a major
feature the surrender of many memory and analytical functions.  It might
be argued, of course, that the opportunity to deal more extensively with
the emotional aspects of disease will compensate the physician for the
expropriation of his diagnostic and therapeutic skills, but it is far
from clear that most physicians are equipped by either temperament or
training to accept change of this kind gracefully.''

}

Although the expression of outright hostility has been replaced to some
extent by curiosity among current medical students, many of whom have
grown up in the presence of computers and with the expectation that they
would use them professionally, the underlying challenge remains: if much
of the analytical ability of the physician can be taken over by
computer, what will the physician do?  Will his or her role really shift
to the more personal aspects of patient care?  Will expertise in
overseeing or further developing computer systems become an essential
skill to the more ambitious students?  Will the exercise of finely-honed
common sense, so that the physician can detect errors in the program, be
among the most highly-valued skills?  Will other activities, such as
basic medical research, occupy more of the students' time?  It is too
early to answer these questions---they are, after all, based on only the
hypothesis that the anticipated technology will come into being---but it
is time to begin asking them, and to plan the future of medical
education with an eye on the coming impact of expert clinical consulting
systems.


\Section Acknowledgments

This research was supported (in part) by the National Institutes of
Health Grant No.\ 1 P01 LM 03374-04 from the National Library of
Medicine.  I also wish to acknowledge many fruitful discussions with
Ramesh Patil, William Long, William Schwartz, Stephen Pauker, and my
many other colleagues and students who have contributed to the
development of these ideas.


\Section References

\input psz;trefer
\input psz;biblia

%\small_size

\refer\Elsta
\au{Elstein, A. S., Shulman, L. A., and Sprafka, S. A.}
\book{Medical Problem Solving: An Analysis of Clinical Reasoning}
\publ{Harvard University Press} \publaddr{Cambridge, Mass.} \year 1978 

\refer\Gorrd
\au{Gorry, G. A., Silverman, H., and Pauker, S. G.}
\paper{Capturing Clinical Expertise:  A Computer Program that Considers
Clinical Responses to Digitalis}
\AJM \vol 64 \pages 452--460 \year{March 1978} 

\refer\Kassa
\au{Kassirer, J. P., and Gorry, G. A.}
\paper{Clinical Problem Solving:   A Behavioral Analysis}
\Annals \vol 89 \pages 245 \year 1978 

\refer\Kulia
\au{Kulikowski, C. A., and Weiss, S. M.}
\selection{Representation of Expert Knowledge for Consultation: The
CASNET and EXPERT Projects}
\editor{Szolovits, P., (Ed.)}
\book{Artificial Intelligence in Medicine}
\publ{Westview Press} \year 1981 

\refer\Longb
\au{Long. W.}
\selection{Criteria for Computer Generated Therapy Advice in a Clinical
Domain}
\book{IEEE Proceedings of Computers in Cardiology
Conference} \publaddr{Williamsburg, Virginia} \year{October 22--24, 1980}

\refer\Morra
\au{Morrell, J., Podlone, M., and Cohen, S. N.}
\paper{Receptivity of physicians in a teaching hospital to a
computerized drug interaction monitoring and reporting system}
\jour{Medical Care} \vol 15 \pages 68--78 \year 1977 

\refer\Neweb
\au{Newell, A., and Simon, H. A.}
\book{Human Problem Solving} \publ{Prentice Hall} \year 1972 

\refer\Patib
\au{Patil, R. S., Szolovits, P. and Schwartz, W. B.}
\selection{Causal Understanding of Patient Illness in Medical Diagnosis}
\IJCAISeven \pages 893--899

\refer\Patic
\au{Patil, R. S.}
\thesis{Causal Representation of Patient Illness for Electrolyte and
Acid-Base Diagnosis}
\MITLCS 
\degree{Ph.D.} \department{Electrical Engineering and Computer Science}
\year{Oct.\ 1981}

\refer\Paukb
\au{Pauker, S. G., Gorry, G. A., Kassirer, J. P., and Schwartz, W. B.}
\paper{Toward the Simulation of Clinical Cognition:  Taking a Present
Illness by Computer}
\AJM \vol 60 \pages 981--995 \year{June 1976}

\refer\Schwa
\au{Schwartz, W. B.}
\paper{Medicine and the Computer:  The Promise and Problems of Change}
\NEJM \vol 283 \pages 1257--1264 \year 1970 

\refer\Swarc
\au{Swartout, W. R.}
\selection{Explaining and Justifying Expert Consulting Programs}
\IJCAISeven

\refer\Szolg
\au{Szolovits, P., (Ed.)}
\book{Artificial Intelligence in Medicine}
\publ{Westview Press} \publaddr{Boulder, Colorado}
\year 1982 \comment{in the AAAS Symposium Series}

\refer\Szoli
\au{Szolovits P., and Long, W. J.}
\selection{The Development of Clinical Expertise in the Computer}
\editor{Szolovits, P., (Ed.)}
\book{Artificial Intelligence in Medicine} \pages 79--117
\publ{Westview Press} \publaddr{Boulder, Colorado} \year 1982 

\refer\Yub
\au{Yu, V. L., Fagan, L. M., Wraith, S. M., Clancey, W. J., Scott,
A. C., Hannigan, J., Blum, R. L., Buchanan, B. G., and Cohen, S. N.}
\paper{Antimicrobial Selection by a Computer: A Blinded Evaluation by
Infectious Diseases Experts}
\JAMA \vol 42 \pages 1279--1282 \year 1979 

\done
