BIOMAT., ART. CELLS, ART. ORG., 15(3), 497-508 (1987)

ARTIFICIAL INTELLIGENCE A N D ARTIFICIAL O R G A N S A N EDITORIAL

Professor J.L. Funck-Brentano Service de Therapeutique Nephrologique Assistance Publique, Hopitaux de Paris

Hopital Necker - 75743 Paris, Cedex 15, France

Artificial intelligence is a new step initiated about 30 years ago within the field of computer sciences. Those who wish to approach the concept of artificial intelligence must clearly differentiate it from the simple management of data which fo r decades covered the practical and useful applications of computer science.

1. MANAGEMENT O F DATA

This is what we can call "classical computer science". It has a restricted field of application in medicine. It concerns only the management of medical activity. This management does not fundamentally d i f fe r from that of a bank, a trade or of any administration, even if it may be more sophisticated within the medical environment. Within the medical field, this technique has alrcndy been successfully applied to the management of hospital administration, the management of pharmacy stocks and food delivery system.

EDITOR'S NOTE: Professor Funck-Brentano is past president of the International Society of Artificial Organs. He has pioneered in the clinical developements of high porosity hemodialysis membranes and the studies of uremic toxins. He has also been deeply involved in the use of computer for medical applications.

497

Copyright 0 1987 by Marcel Dekker, Inc.

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498 EDITORIAL

Data banks can be filled with data. These data retain their own significance no matter what processing systems are applied to them. The programs lead to a conclusion which cannot be contested. Its conclusion finds its justification within the program itself. The program is a deterministic data transformer whose output is considered as information. This closed loop system appears to ve very efficient for management of hospital administration networks. As in the case of airline tickets delivery, it takes advantage of the huge memory capacity of the computer and of its very rapid data processing capabilities.

The weakness of this procedure appears when it is applied to an intellectual medical activity such as diagnosis. Despite the enormous amount of work which has been made in computer assisted diagnosis, the results a re very poor. This could be predicted because:

- The significance of a data in medicine is scarcely independent of the context in which it has been observed. Blood urea of 0.50 g/L as such m.ay correspond both to healthy kidney function and to impaired kidney function.

- When questioning the data base after examining his patient, the doctor may omit one of the key symptoms for diagnosis, for example, the search for K.B. in pulmonary tuberculosis.

- At the time the doctor has decided to start the program it runs whatever was introduced and questioned in the computer.

- Thus, in many cases the diagnosis which is given a t the end of the procedure is false or a t least uncertain. It does not f i t with the diagnosis made by an expert in the field.

Such a procedure, makes the doctor feel frustrated by the computer. In fact the computer does not assist the physician in making a diagnosis but makes the diagnosis itself, substituting for a specific medical approach.

It may be concluded that the management of data isolated from their context and their meaning does not usually f i t with what practitioners expect from a computer assisted diagnosis system.

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EDITORIAL

2. ARTIFICIAL INTELLIGENCE

499

What d o we mean exactly by ar t i f ic ia l intelligence? Winston def ined Art i f ic ia l Intelligence a s the "study of ideas

tha t enable computers to be or to seem intelligent". One of i ts theoritical goals is to understand some of the principles t h a t make intelligence possible.

century, there is a great relativism i n the concept of intelligence: "The extent to which we regard something as behaving i n a n intelligcnt manner is determined as much by our own s ta te of mind a n d t ra in ing as by the properties of the object under consideration. If we a r e able to explain the behaviour of a n object, we have little temptat ion to imagine intelligence ..."

If we simply def ine hats as simple facts, information as what can be extracted f r o m a sum of d a t a a n d knowledne as o u r unders tanding of a sum of information integrated i n what we already know, knowledge management appears to play a central role i n ar t i f ic ia l intelligence.

appears as a new emerging scient i f ic discipline, having a s t rong connection with psychology (of ten referred t o a s cognitive science), philosophy, linguistics and, of course, computer sciences.

T h e present appl icat ion of Art i f ic ia l Intelligence techniques includes language understanding, image understanding, problem solving, planning a n d expert systems a n d f ina l ly learning.

We will here mainly focus on expert systems as they a r e cer ta inly the domain which have received the greatest a t tent ion f r o m physicians. This is easily explained by the f a c t tha t expert systems represent a domain of appl icat ion of Art i f ic ia l Intelligence where the most tangible results, i n terms of benefi ts f o r pat ient management, have been obtained.

However, a s we expressed by Tur ing i n t h e middle of this

From a n epistemological point of view, a r t i f ic ia l intelligence

3. REPRESENTATION OF KNOWLEDGE

Most "state o f the ar t" expert systems a r e f requent ly able to manipulate two k inds of knowledge: - -

dynamic knowledge, generally i n the f o r m of product ion rules a n d more s ta t ic knowledge, generally in the f o r m of semantic networks and/or frames.

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500 EDITORIAL

Rules correspond pretty well to a common form of medical reasoning: deductive reasoning. They combine IF and THEN clauses which might correspond:

observed (for examplc if the blood prcssure is bclow 70 mmHg, call the emergency room). or more generally to a premise and a conclusion (if the pH is low, diagnosis is acidosis).

- to a situation and an action to perform if thc situation is

-

(i). Semantic nets

describe the relationships between the nodes. Examples of nodes are medical concepts such as a symptom, a diagnosis or a treatment.

Arcs are used to represent hierarchies (glomerulonephritis IS a kidney disease; CORTEX IS A PART of the kidney) but also to represent causal, spatial or temporal relationships.

Are constituted by sets of nodes interconnected by arcs which

(ii). Causal Arcs Are extensively used in the semantic network of the ABEL expert

for the diagnosis of acid-base and electrolyte disorders. Such arcs are useful to model physiological mechanisms, such as causal relations or feedback mechanisms.

(iii). Frames Have many features in common with the semantic network where each

node or frame is a complex object with relationship with other frames. Thus, the sum of constructs information. Knowledge can be

defined as a set of information related together in order to become knowledge. Representations of knowledge are multiple but all contain both data, information and relationships between them. This is, in a way, the anatomy of Artificial Intelligence.

4. INTELLIGENT BEHAVIOUR (Inference Engine)

In order to become usable, intelligent behaviour necessitates the

In Artificial Intelligence the most common procedure to achieve intervention of reasoning mechanisms.

reasoning is represented by the inference engine.

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EDITORIAL 501

EXPERT SYSTEMS : THE INFEREflCE ENGINE

FORWARD AND BACKWARD CHAINING

PF I-> D- C-

E-

l I AUTOMATING KNOWLEDGE INFERENCE

C L I N I C A L CASES

KNOWLEDGE BASE 1

HYPOTHESIS

STATIST ICAL PACKAGES HYPOTHESIS

EVALUATION

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502 EDITORIAL

Most advanced systems combine f o r w a r d a n d backward chaining. Forward chaining corresponds to s t r ic t deduct ive reasoning. Star t ing f r o m ini t ia l facts, t h e system tries, by applying its knowledge, to deduce a maximum of information. For example, i f we suppose t h a t A, C a n d E a r e true, the system will successively deduct B f r o m A, then D f r o m B a n d C, a n d f ina l ly F f r o m D a n d E.

t h a t the order i n which t h e rules a r e appl ied is not f ixed by a n algori thm but depends on t h e t rue values of premises.

It confirms or inf i rms goals.

have to prove tha t D and E a r e t r u e and consequently t h a t B, C a n d E a r e truc, a n d so on. Backward chaining is extensively used i n logic programming such a s i n the PROLOG language. This shows how forward a n d backward mechanisms can be integrated into a n expert system. Consider the medical record of a given patient. This medical record can be extracted f r o m a pat ient d a t a base to constitute a fac tua l da ta base or i t can be direct ly entered by the physician. By using a f o r w a r d o r da ta dr iven reasoning, the expert system might suggest some possible hypotheses, thus reducing the total number of possible goals. Then , using backward o r goal-driving reasoning, the expert system might t ry t o discr iminate between goals by requir ing complementary information, thus reducing again the search space a n d so on.

T h e main d i f fe rence with programming languages lies i n the f a c t

T h e value of reasoning can be demonstrated by backward chaining.

Suppose tha t we want to prove tha t F is true. This means t h a t we

5. ARTIFICIAL INTELLIGENCE SOFTWARE

Optimizat ion of expert systems is induced by a kind of shr inkage between t h e ent i t ies described a n d the t reatments they may undergo. It leads to the concept of obiect, each one being representat ive of a set which is organized a s a network of characteristics a n d behaviour. Knowledge takes over the t reatment processing. Knowledge representation is n o longer exclusively numeric i n order t o become symbolic.

but become what is called knowledge bases. These new bases contain knowledge such as production rules which a r e organized according to

Within these new expert systems, d a t a bases a r e n o longer fac tua l

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EDITORIAL 503

various syntaxes. In this case the question may omit a specific data. It can regain this data inside the rule that is questioned.

But, above all, manipulation of knowledge requires the creation of new languages which are much more transparent than earlier ones and thus much more flexible. This flexibility makes them much easier to use. An expert system is able, among a mass of potential hypotheses, to extract the few which may lead to the solution.

It acts as a genuine intellectual prosthesis. The problem for us is to determine if expert systems may be

useful in artificial organ practice and if they are to be developped in this field of medicine.

JUSTIFICATION AND LIMITATION OF THE DEVELOPMENT OF EXPERT SYSTEMS IN MEDICINE

General justifications or incitements to the devlopment of expert systems have been given by Waterman in a recent book entitled "A Guide to Expert Systems". He summarizes five main justifications:

- -

when expertise is scarce, only shared by a few number of people when expterise is needed in many locations such as the management

when expertise is needed in a hostile environment such as in space

finally when the task solution has a high pay off.

of a very frequent conditions

or a nuclear power station. -

-

However, the fact that a development has some justification does

It is commonly admitted that the factors to be in favour of the not mean that the development is feasible.

development of an expert system are:

- the task is primarily cognitive by opposition to driving a car or

the task requires specialized knowledge contrary to the common

the task is not too difficult because the expert system will never

riding a bicycle

sense knowledge used in a conversation

be completed but also not too easy because the Expert System is not needed in such cases

- -

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504 EDITORIAL

- they are recognized experts and those experts have sufficient willingness to share their knowledge and in formalizing their knowledge

question that may be considered as hazardous but to which I believe that we can give a positive answer in our own field.

- and finally the expert is probably better than amateurs, a

All these justifications are present for the application of Expert System in the domain of artificial organs.

Then rises the question: in what fields are Expert System applicable and fruitful in the field of artificial organs? Three main fields in artificial organs are well adapted for this purpose: 1) addition of interpretation processes on the apparatus, 2) computer assisted teaching and 3) modelling.

1. ADDITION OF INTERPRETATION PROCESS ON THE APPARATUS

Expert Systems can presently be added to an apparatus in order to obtain automatic interpretation of the data produced by the apparatus.

A good example is automatic interpretation of the electrocardiogram (E.C.G.). Such a system already exists.

It presently works as a feed back loop system very close to the traditional data management system. The addition of expert systems within the procedure extend the scope of interpretation. I t extends the flexibility of interpretation and makes it available for more electrocardiogram patterns and usable by nonspecialists in cardiology.

than 90 per cent of cases. This performance is that of the best cardiologists in the world, which means it is better than that of most of practicing cardiologists.

becomes available to all practitioners as long as they know the inference of such an interpretation on the treatment of the patient. It must be stressed that an additive Expert System may give this inference to the practitioner. Then, the market open to electrocardiography is no longer limited to that of cardiologists alone but can be extended to that of practitioners who wish to use it

It is generally accepted that it gives exact conclusions in more

The consequences of such an addition is huge. The use of E.C.G.

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EDITORIAL 505

on their patients. It enhances the competence of general practitioners.

System to artificial organs. In the artificial kidney, for example, the sodium concentration in the bath may be determined for any ultrafiltration volume according to the necessity of maintaining a plasma sodium water concentration around the normal value of 142 mOs/l. Using an expert system opens the possibility to adapt the procedure to all situations encountered in all treatment situations. Another step could bc complete automation of sodium delivery to the dialysate.

increased by such a procedure leading to a n extension of artificial kidney practice at home.

Imaging interpretation could benefit from such a process. Ultrasonic echography, for example, could be automatically interpreted for one such specific application such as the obstetrical use of echography.

mainly NMR, can take advantage of expert system addition, thus increasing the field of their application.

could benefit from the incorporation of Expert System for interpretation of the result of their procedure, but it is easy to imagine such a list and its future.

This shows the profile of what can be done by adding Expert

One can imagine how much the patient's security could be

Presently, most imaging processings a re digital. All of them,

I cannot give you a prospective list of artificial organs which

2. COMPUTER ASSISTED TEACHING (C.A.T.)

Expert System opens the field of EDUCATION and extends it to a

In all diseases that have to be controlled day after day by less sophisticated population than it presently reactes.

artificial organs and managed by the patient himself, the need for teaching becomes a crucial problem.

The first Computer Assisted Teaching systems presently available give a poor image of what can be done. Most of them are built on the basis of multiple choice questions. This is a poor system that does not prejudice what will become feasible with new Expert System adapted for Computer Assisted Teaching.

adaptable to all kinds of teaching targets. Flexibility is the key word for these new systems. They will be

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5 06 EDITORIAL

In artificial organs, the main target for teaching is the patient himself. He has to control his own treatment, within his own capacity to do so. For many artificial organs the market is no longer represented only by physicians or surgeons but by the huge number of patients who need to manage them.

3. APPLICATION OF MODELLING

Although modelling in artificial kidney practice has existed now for 10 to IS years, i t never really made its way into practical medicine.

There are many reasons for this reluctance. The concept of modelling to approach multifactorial problems is not yet clearly understood by the medical hemodialysis community. Most of the parameters involved in such a procedure a re difficult to record. But the main reason may be that integration of models in the general clinical and biological analysis is difficult to achieve. Because they are flexible and can be adapted to many specific situations, expert systems may be a useful tool for integrating this approach into the current clinical behaviour in the field of hemodialysis.

THE LAST QUESTION I WOULD LIKE TO RAISE IS: HOW CAN WE ACHIEVE THESE GOALS?

The answer must take into account the specificity of the Artificial Intelligence product which has to be produced in order to achieve a specific goal.

This product has to associate many conditions including coordination of various people:

1. Expert System as a language must be made or adapted from existing Expert System by highly specialized computer scientists. These humans a re researchers who usually work at night, sleeping during the day and who communicate around the world through satellite networks.

2. They need sophisticated and expensive computers to be available to them at any time. This means that these big computers have to be

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EDITORIAL 50 7

stored in one place with a set of people who a r e ab le to assume the maintenance of the equipment .

3. When they have built u p a language, the i r instinctive desire is t o improve i t ra ther than making it avai lable f o r application. T h e n one of the main problems f o r us is to pick u p these languages i n order to f e e d them with expertise data . Experts usually have less knowledge in computer science than d i d the former pure computer scientists. But they must be able to ta lk together i n order to create the language in such a way t h a t it becomes well adapted to the purpose of the expert.

4. At this stage of the procedure a f i r s t prototype is bu i l t up. Much work has to be done before this prototype is accura te enough to be marketed a n d used by the users.

5. Before reaching the stage of a marketable product, t h e prototype has to be evaluated. I t must be ver i f ied tha t most of t h e experts agree on the expertise a n d tha t the number of bugs i n t h e program is suff ic ient ly low to allow f i r s t marketing.

6. Then the market ing s t ructure must be found.

All these conditions a r e very rarely met i n a n y country. Because of the wide scope of specialists needed to achieve this goal, academic a n d pr ivate organizations must join their e f fors t to succeed which, a t least in France, is not easy.

T h e t rend in this direct ion must be given by a l l t h e communit ies t h a t clearly believe that their members need these new tools t o progress i n the i r f ie ld a n d to better extend their knowledge for bet ter pat ient care. I hope tha t you will share my conviction t h a t this is the case f o r t h e f ie ld of a r t i f ic ia l organs.

ACKNOWLEDGEMENT

I a m not i n a n y way a computer scientist. Perhaps i t is because I a m not a computer scientist tha t I may have a chance to convince you t h a t

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508 EDITORIAL

the introduction of artificial intelligence is a fundamental step for the extension of artificial organs.

Because I am not a computer scientist I did need help from such a scientist and, I wish to deeply thank Pr Patrice DEGOULET for giving me his friendly help in writing this paper.

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