5P41-RR00785-12 Description of Program Activities

II. Description of Program Activities

This section corresponds to the predefined forms required by the Division of Research
Resources to provide information about our resource activities for their computerized
retrieval system. These forms have been submitted separately and are not reproduced
here to avoid redundancy with the more extensive narrative information about our
resource and progress provided in this report.

II.A. Scientific Subprojects
Our core research and development activities are described starting on page 18, our

training activities are summarized starting on page 49, and the progress of our
collaborating projects is detailed starting on page 85.

II.B. Books, Papers, and Abstracts

The list of recent publications for our core research and development work starts on
page 45 and those for the collaborating projects are in the individual reports starting on
page 85.

II.C. Resource Summary Table

The details of resource usage, including a breakdown by the various subprojects, is given
in the tables starting on page 52.

3 E. H. Shortliffe
5P41-RR00785-12 Narrative Description

II. Narrative Description

We are about to start the final grant year in the current SUMEX-AIM award. This
annual report was prepared in parallel with a competing renewal proposal for
continuing the resource beyond July 1986. The report is based on the comprehensive
progress sections of the proposal for resource core research and development and for
the collaborating scientific community.

I].A. Summary of Research Progress

IIY.A.1. Executive Summary

This summary provides an overview of our accomplishments. In the almost twelve
years since the SUMEX-AIM resource was established, computing technology and
biomedical artificial intelligence research have undergone a remarkable evolution. As
we prepare to renew the resource through the remainder of the 1980's, we take pride in
the realization that SUMEX has both influenced and Tesponded to those changing
technologies. It is widely recognized that our resource has fostered highly influential
work in medical AI -- work from which it is generally acknowledged that the expert
systems field emerged -- and that it has simultaneously helped define the technological
base of applied AI research. The LISP machines to which we directed our attention in
1980 have now demonstrated their practicality as research tools and, increasingly, as
potential mechanisms for disseminating AI systems as cost-effective decision aids in
clinical settings such as private offices. We look forward to another half decade during
which the era of centralized machines for AI research will come to an end, having been
supplanted by networks of distributed and heterogeneous single-user machines sharing
common resources such as file servers, printers, and gateways to other local and long-
distance networks.

We continue to be motivated by three main goals:

l. to develop and provide impeccable computing resources and human
assistance to scientists working on applications of artificial intelligence
research in medicine and biology;

2. to demonstrate that it is feasible to provide resources and assistance to a
national community of researchers from a central site, integrating distributed
and centralized computing technology, local and national computer
communication networks, and a staff oriented toward the special problems
of individuals participating in AIM research at other institutions;

3. to develop the community of scientists interested in working on applications
of AI to the biomedical sciences; facilitating the growth, health, and vigor of
the community by providing electronic communications that link its
members and by assisting with the dissemination of systems software and
applications programs that are of use to the wider community of AIM
researchers. One question we have been asking is, "Is there a new style of
science that will emerge in a communications-enhanced Setting of national,
tather than institutional, scope?” Within a decade it was clear that the
answer to this question was (and is) “yes”!

5 E. H. Shortliffe
Executive Summary $P41-RR00785-12

SUMEX's Success as a National Research Resource

The SUMEX Project has demonstrated that it is possible to operate a computing
research resource with a national charter and that the services providable over networks
were those that facilitate the growth of Al-in-Medicine. Many NIH computer RR's
have been mostly institutional in scope, occasionally regional (like the UCLA resource).
SUMEX now has the reputation of a model national resource, pulling together the best
available interactive computing technology, software, and computer communications in
the service of a national scientific community. Planning groups for national facilities
in cognitive science, computer science, and biomathematical modeling have discussed
and studied the SUMEX model and new resources, like the recently instituted BIONET
resource for molecular biologists, are closely patterned after the SUMEX example.

A decade ago, when machines up to the task of supporting AI research cost $1M, some
of the most notable projects in the history of Artificial Intelligence were done with
terminal-and-network, without a computer on site. In human terms, this meant, of
course, not having the headaches and energy drains of proposing a machine, installing
it, maintaining it and its software, hiring its system programmers and operators, dealing
with communication vendors, etc. The famous INTERNIST program was developed
from Pittsburgh in this way. And the ACT computer model was begun at Michigan,
continued at Yale, and later at Carnegie-Mellon, all without moving the program or
losing a day's work because of machine transition problems. The GENET community
of over 300 molecular biologists grew up in a year around SUMEX programs for
analyzing DNA sequences. Their demand for these centralized capabilities ultimately
swamped our machine and led to the initiation of a separate resource (BIONET) to
meet their needs.

The projects SUMEX supports have generally required substantial computing resources
with excellent interaction. Even today though, with the growing availability of Lisp
workstations, this computing power is still hard to obtain in all but a few universities.
SUMEX is, in a sense, a "great equalizer". A scientist gains access by virtue of the
quality of his/her research ideas, not by the accident of where s/he happens to be
situated. In other words, the resource follows the ethic of the scientific journal.

SUMEX has demonstrated that a computer resource is a useful "linking mechanism" for
bringing together and holding together teams of experts from different disciplines who
share a common problem focus. For example, computer scientists have been
collaborating fruitfully with physical chemists, molecular biochemists, geneticists,
crystallographers, internists, ophthalmologists, infectious disease specialists, intensive
care specialists, oncologists, psychologists, biomedical engineers, and other expert
practitioners. And in some of these cases, the interdisciplinary collaboration, usually so
difficult to achieve in the best of circumstances, was achieved in spite of geographical
distance between the participants, using the computer networks.

SUMEX has also achieved successes as a community builder. AI concepts and software
are among the most complex products of computer science. Historically it has not been
easy for scientists in other fields to gain access to and mastery of them. Yet the
collaborative outreach and dissemination efforts of SUMEX have been able to bridge
the gap in numerous cases. Over 36 biomedical AI application projects have developed
in our national community and have been supported by SUMEX over the years. And 9
of these have matured to the point of now continuing their research on facilities
outside of SUMEX. For example, the BIONET resource (named GENET while at
SUMEX) is being operated by IntelliCorp; the Rutgers Computers in Biomedicine
resource is centered at Rutgers University; the CADUCEUS project splits their research
work between their own VAX computer and the SUMEX resource; and the Chemical
Synthesis project now operates entirely on a VAX at U.C. Santa Cruz.

The SUMEX mission has been able to capture the contributions of some of the finest

E. H. Shortliffe 6
5P41-RR00785-12 Executive Summary

computers-in-medicine specialists and computer scientists in the country. For example,
Professor Joshua Lederberg (SUMEX’s first PI, now President of The Rockefeller
University) is a member of SUMEX's Executive Committee; and Dr. Donald Lindberg,
former Director of the University of Missouri's Medical Information Science group, and
now Head of the National Library of Medicine, was until recently the Chairman of the
AIM Advisory Group. Professor Herbert Simon of Carnegie-Mellon University,
Professor Marvin Minsky of MIT, and many other distinguished scientists serve on that
peer review committee.

SUMEX and Artificial Intelligence Research

The SUMEX Project is a relative latecomer to AI research. Yet its scope has given
strong impetus to this historic development in applied computer science. AI research is
that part of computer science that investigates symbolic reasoning processes, and the
representation of symbolic knowledge for use in inference. It views heuristic or
judgmental knowledge to be of equal importance with "factual" knowledge, indeed to be
the essence of what we call “expertise”. In its "Expert Systems” work, it seeks to
capture the expertise of a field, and translate it into programs that will offer intelligent
assistance to a practitioner in that field.

For computer applications in medicine and biology, this research path is crucial, indeed
ineluctable. Medicine and biology are not presently mathematically-based sciences:
unlike physics and engineering, they are seldom capable of exploiting the mathematical
characteristics of computation. They are essentially inferential, not calculational,
sciences. If the computer revolution is to affect biomedical scientists, computers will
be used as inferential aids.

Perhaps the larger impact on medicine and biology will be the exposure and refinement
of the hitherto largely private heuristic knowledge of the experts of the various fields
studied. The ethic of science that calls for the public exposure and criticism of
knowledge has traditionally been flawed for want of a methodology to evoke and give
form to the heuristic knowledge of scientists. The AI methodology is beginning to fill
that need. Heuristic knowledge can be elicited, studied, critiqued by peers, and taught
to students.

The tide of AI research and application is rising. AI is one of the principal fronts
along which university computer science groups are expanding. Federal and industrial
support for AI research is vigorous and growing, although support specifically for
biomedical applications continues to be limited. The pressure from student career-line
choices is great: to cite an admittedly special case, approximately 80% of the students
applying to Stanford's computer science Ph.D. program cite AI as a possible field of
specialization (up from 30% 4 years ago). At Stanford, we have vigorous special
programs for student training and research in AI -- a new graduate program in Medical
Information Sciences and the two-year Masters Degree in AI program. All of these
have many more applicants than available slots. Demand for our graduates, in both
academic and industrial settings, is so high that students typically begin to receive
solicitations one or two years before completing their degrees.

There is an explosion of interest in medical AI. The American Association for
Aftificial Intelligence (AAAI), the principal scientific membership organization for the
Al field, has 7000 members, over 1000 of whom are members of the medical special
interest group known as the AAAI-M. Speakers on medical AI are prominently
featured at professional medical meetings, such as the American College of Pathology
and American College of Physicians meetings; a decade ago, the words “artificial
intelligence” were never heard at such conferences. And at medical computing
meetings, such as the annual Symposium on Computer Applications in Medical Care
and the international MEDINFO conferences, the growing interest in AI and the rapid

7 E. H. Shortliffe
Executive Summary >P41-RRO00785-12

increase in papers on AI and expert systems are further testimony to the impact that
the field is having.

Al is beginning to have a similar effect on medical education. Such diverse
organizations as the National Library of Medicine, the American College of Physicians,
the Association of American Medical Colleges, and the Medical Library Association
have all called for sweeping changes in medical education, increased educational use of
computing technology, enhanced research in medical computer science, and career
development for people working at the interface between medicine and computing.
They all cite evolving computing technology and (SUMEX-AIM) AI research as key
motivators.

In industry, AI is on an exponential growth path as well. In the USA alone, over 30 AI
Start-up companies have been formed in the past four years and many groups have
been established. in large companies as well. The list of names is long and includes
Hewlett-Packard, Schlumberger (including Fairchild), Texas Instruments, Xerox, IBM,
DEC, General Motors, General Electric, Boeing, Rockwell, FMC Corp, Ford-Aerospace,
Apple Computer, Teknowledge, InteiliCorp, Syntelligence, Lucid, Inference Corp,
Symbolics, LMI, and so on... Many of these firms are marketing hardware and software
tools for expert system development, as well as custom system services. And Japan has
mounted a long-term, well-funded "Fifth Generation" computing effort to broadly
eevelon knowledge-based systems technology as part of their national economic base of
the 1990's,

The AI tide is rising largely because of the development in the 1970's and early 1980's
of methods and tools for the application of AI concepts to difficult professional-level
problem solving. Their impact was heightened because of the demonstration in various
areas of medicine and other life sciences that these methods and tools really work.
Here SUMEX has played a key role, so much so that it is regarded as "the home of
applied AI."

SUMEX has been the nursery, as well as the home, of such well-known AI systems as
DENDRAL (chemical structure elucidation), MYCIN (infectious disease diagnosis and
therapy), INTERNIST (differential diagnosis), ACT (human memory organization),
ONCOCIN (cancer chemotherapy protocol advice), SECS (chemical synthesis), EMYCIN
(rule-based expert system tool), and AGE (blackboard-based expert system tool). In the
past four years, our community has published a dozen books that give a scholarly
perspective on the scientific experiments we have been performing. These volumes, and
other work done at SUMEX, have played a seminal role in structuring modern AI
paradigms and methodology. First among these scientific directions has been a switch
in AI's focus from inference procedures to knowledge representation and use. There is
now a recognition that the power of problem solvers derives primarily from the
knowledge that they contain -- of the elements of the problem domain, of the strategies
for solving problems in that domain, and of the forms in which the knowledge is to be
acquired. In 1977, Goldstein and Papert of MIT, writing in the journal Cognitive
Science, described the change of focus as a “paradigm shift” in AI. This shift was
induced largely (though, of course, not exclusively) by the work at SUMEX, beginning
with the DENDRAL development in 1965.

Toward the ‘90s: the Future of SUMEX

Given this setting of success and vitality, what is the future need and course for
SUMEX as a resource -- especially in view of the on-going revolution in computer
technology and costs, with the emergence of powerful single-user workstations and local
area networking? The answers remain clear.

At the deepest research level, despite our considerable success in working on medical

E. H. Shortliffe 8
5P41-RR00785-12 Executive Summary

and biological applications, the problems we can attack are still sharply limited. Our
current ideas fall short in many ways against today’s important health care and
biomedical research problems brought on by the explosion in medical knowledge and
for which AI should be of assistance. Just as the research work of the 70's and 80's in
the SUMEX-AIM community fuels the current practical and commercial applications,
our work of the late 80's will be the basis for the next decade's systems. Our growing
knowledge is clearly attained in an incremental fashion; we build today on the results
of the past decade, and we will build in the 1990's on the work we undertake today.

At the resource level, there is a growing, diverse, and active AIM research community
with intense needs for computing resources to continue its work. Many of these groups
still are dependent on the SUMEX-AIM resources. For those who have been able to
take advantage of newly developed local computing facilities, SUMEX-AIM provides a
central cross-roads for communications and the sharing of programs and knowledge. In
its core research and development role, SUMEX-AIM has its sights set on the hardware
and software systems of the next decade. We expect major changes in the distributed
computing environments that are just now emerging in order to make effective use of
their power and to adapt them to the development and dissemination of biomedical AI
systems for professional user communities. In its training role, SUMEX is a crucial
resource for the education of badly needed new researchers and professionals to
continue the development of the biomedical AI field. The "critical mass" of the
existing physical SUMEX resource, its development staff, and its intellectual ties with
the Stanford Knowledge Systems Laboratory (previously called the Heuristic
Programming Project), make this an ideal setting to integrate, experiment with, and
export these methodologies for the rest of the AIM community.

9 E. H. Shortliffe
Resource Goals and Definitions $P41-RR00785-12

IIJ.A.2. Resource Goals and Definitions

SUMEX-AIM is a national computer resource with a multiple mission: a) promoting
experimental applications of computer science research in artificial intelligence (AI) to
biological and medical problems, b) studying methodologies for the dissemination of
biomedical AI systems into target user communities, c) supporting the basic AI research
that underlies applications, and d) facilitating network-based computer resource sharing,
collaboration, and communication among a national scientific community of health
research projects. The SUMEX-AIM resource is located physically in the Stanford
University Medical School and serves as a nucleus for a community of medical AI
projects at universities around the country. SUMEX provides computing facilities tuned
to the needs of AI research and communication tools to facilitate remote access,
inter- and intra-group contacts, and the demonstration of developing computer
programs to biomedical research collaborators.

III.A.2.1. What is Artificial Intelligence?

Artificial Intelligence research is that part of Computer Science concerned with symbol
manipulation processes that produce intelligent action [1, 26, 29, 35]. Here intelligent
action means an act or decision that is goal-oriented, is arrived at by an understandable
chain of symbolic analysis and reasoning steps, and utilizes knowledge of the world to
inform and guide the reasoning.

Placing Al in Computer Science

A simplified view relates AI research with the rest of computer science. The manner
of use of computers by people to accomplish tasks can be thought of as a one-
dimensional spectrum representing the nature of the instructions that must be given the
computer to do its job. At one extreme of the spectrum, Tepresenting early computer
science, the user supplies his intelligence to instruct the machine precisely Aow to do the
job, step-by-step.

At the other extreme of the spectrum, the user describes what he wishes the computer to
do for him to solve a problem. He wants to communicate what is to be done without
having to lay out in detail all necessary subgoals for adequate performance, yet with a
reasonable assurance that he is addressing an intelligent agent that is using knowledge
of his world to understand his intent, complain or fill in his vagueness, make specific
his abstractions, correct his errors, discover appropriate subgoals, and ultimately
translate what he wants done into detailed processing steps that define how it should be
done by a real computer. The user wants to provide this specification of what to do in
a language that is comfortable to him and the problem domain (perhaps English) and
via oe) mication modes that are convenient for him (including perhaps speech or
pictures).

Progress in computer science may be seen as steps away from that extreme how point
on the spectrum: the familiar panoply of assembly languages, subroutine libraries,
compilers, extensible languages, etc. illustrate this trend. The research activity aimed at
creating computer programs that act as intelligent agents near the what end of the
spectrum can be viewed as a long-range goal of AI research.

Expert Systems and Applications

The national SUMEX-AIM resource has in large part made possible a_ long,
interdisciplinary line of artificial intelligence research at Stanford concerned with the
development of concepts and techniques for building expert systems [15]. An expert

E. H. Shortliffe 10
5P41-RR00785-12 Resource Goals and Definitions

system is an intelligent computer program that uses knowledge and inference procedures
to solve problems that are difficult enough to require significant human expertise for
their solution. For some fields of work, the knowledge necessary to perform at such a
level, plus the inference procedures used, can be thought of as a model of the expertise
of the expert practitioners of that field.

The knowledge of an expert system consists of facts and heuristics. The facts
constitute a body of information that is widely shared, publicly available, and generally
agreed upon by experts in a field. The Aeuristics are the mostly-private, little-discussed
Tules of good judgment (rules of plausible reasoning, rules of good guessing) that
characterize expert-level decision making in the field. The performance level of an
expert system is primarily a function of the size and quality of the knowledge base that
it possesses.

Projects in the SUMEX-AIM community are concerned in some way with the
application of AI to biomedical research. Brief abstracts of the various projects
currently using the SUMEX resource can be found in Appendix C on page 215 and
more detailed progress summaries in Section IV on page 85. The most tangible
Objective of this approach is the development of computer programs that will be more
general and effective consultative tools for the clinician and medical scientist. There
have already been promising results in areas such as chemical structure elucidation and
synthesis, diagnostic consultation, molecular biology, and modeling of psychological
processes,

Needless to say, much is yet to be learned in the process of fashioning a coherent
scientific discipline out of the assemblage of personal intuitions, mathematical
procedures, and emerging theoretical structure comprising artificial intelligence research.
State-of-the-art programs are far more narrowly specialized and inflexible than the
corresponding aspects of human intelligence they emulate; however, in special domains
they may be of comparable or greater power, eg., in the solution of structure problems
7 organic chemistry or in the rigorous consideration of a large diagnostic knowledge
ase,

ll E. H. Shortliffe
Resource Goals and Definitions 5P41-RR00785-12

III.A.2.2. Resource Sharing

An equally important function of the SUMEX-AIM resource is an exploration of the
use of computer communications as a means for interactions and sharing between
geographically remote research groups engaged in biomedical computer science research
and for the dissemination of AI technology. This facet of scientific interaction is
becoming increasingly important with the explosion of complex information sources
and the regional specialization of groups and facilities that might be shared by remote
researchers [19, 5]. And, as projected earlier, we are seeing a growing decentralization
of computing resources with the emerging technology in microelectronics and a
correspondingly greater role for digital communications to facilitate scientific exchange.

Our community building effort is based upon the developing state of distributed
computing and communications technology. While far from perfected, these capabilities
offer highly desirable latitude for collaborative linkages, both within a given research
project and among them. A number of the active projects on SUMEX are based upon
the collaboration of computer and medical scientists at geographically separate
institutions, separate both from each other and from the computer resource (see for
example, the MENTOR and PathFinder projects).

In the early 1970's, the initial model for SUMEX-AIM as a centralized resource was
based on the high cost of powerful computing facilities and the infeasibility of being
able to duplicate them readily. As planned, this central role has already evolved
significantly and continues to evolve with the introduction of more compact and
inexpensive computing technology now available at many more research sites. At the
same time, the number of active groups working on biomedical AI problems has grown
and the established ones have increased in size. This has led to a growth in the
demand for computing resources far beyond what SUMEX-AIM could reasonably and
effectively provide on a national scale. We have actively supported efforts by the more
mature AIM projects to develop or adapt additional computing facilities tailored to
their particular needs and designed to free the main SUMEX resource for new,
developing applications projects. To date, over 10 of the national projects have moved
some or all of their work to local sites and several have begun resource communities of
their own (see page 79). Thus, as more remotely available resources have become
established, the balance of the use of the SUMEX-AIM resource has shifted toward
supporting start-up pilot projects and the growing AI research community at Stanford.

E. H. Shortliffe 12
5P41-RR00785-12 Resource Goals and Definitions

IIT.A.2.3. Significance to Biomedicine

Artificial intelligence is the computer science of representations of symbolic knowledge
and its use in symbolic inference and problem-solving processes. There is a certain
inevitability to this branch of computer science and its applications, in particular, to
medicine and biosciences. The cost of computers will continue to fall drastically during
the coming two decades. As it does, many more of the practitioners of the world's
professions will be persuaded to turn to economical automatic information processing
for assistance in managing the increasing complexity of their daily tasks. They will
find, from most of computer science, help only for those problems that have a
mathematical or statistical core, or are of a routine data-processing nature. But such
problems will be relatively rare, except in engineering and physical science. In
medicine, biology, management, indeed in most of the world's work, the daily tasks are
those requiring symbolic reasoning with detailed professional knowledge. The
computers that will act as intelligent assistants for these professionals must be endowed
with symbolic reasoning capabilities and knowledge.

The growth in medical knowledge has far surpassed the ability of a single practitioner
to master it all, and the computer's superior information processing capacity thereby
offers a natural appeal. Furthermore, the reasoning processes of medical experts are
poorly understood; attempts to model expert decision-making necessarily require a
degree of introspection and a structured experimentation that may, in turn, improve the
quality of the physician's own clinical decisions, making them more reproducible and
defensible. New insights that result may also allow us more adequately to teach medical
students and house staff the techniques for reaching good decisions, rather than merely
to offer a collection of facts which they must independently learn to utilize coherently.

The knowledge that must be used is a combination of factual knowledge and heuristic
knowledge. The latter is especially hard to obtain and represent since the experts
providing it are mostly unaware of the heuristic knowledge they are using. Medical and
scientific communities currently face many widely-recognized problems relating to the
rapid accumulation of knowledge, for example:

e codifying theoretical and heuristic knowledge

e effectively using the wealth of information implicitly available from
textbooks, journal articles and other practitioners

e disseminating that knowledge beyond the intellectual centers where it is
collected

* customizing the presentation of that knowledge to individual practitioners as
well as customizing the application of the information to individual cases

We believe that computers are an inevitable technology for helping to overcome these
probiems. While recognizing the value of mathematical modeling, statistical
classification, decision theory and other techniques, we believe that effective use of such
methods depends on using them in conjunction with less formal knowledge, including
contextual and strategic knowledge.

Artificial intelligence offers advantages for representing and using information that will
allow physicians and scientists to use computers as intelligent assistants. In this way we
envision a significant extension to the decision-making powers of specific practitioners
without reducing the importance of those individuals in that process.

Knowledge is power, in the profession and in the intelligent agent. As we proceed to
model expertise in medicine and its related sciences, we find that the power of our

13 E. H. Shortliffe
Resource Goals and Definitions §P41-RR00785-12

programs derives mainly from the knowledge that we are able to obtain from our
collaborating practitioners, not from the sophistication of the inference processes we
observe them using. Crucially, the knowledge that gives power is not merely the
knowledge of the textbook, the lecture and the journal, but the knowledge of good
practice~-the experiential knowledge of good judgment and good guessing, the
knowledge of the practitioner's art that is often used in lieu of facts and tigor. This
heuristic knowledge is mostly private, even in the very public practice of science. It is
almost never taught explicitly, is almost never discussed and critiqued among peers, and
most often is not even in the moment-by-moment awareness of the practitioner.

Perhaps the the most expansive view of the significance of the work of the SUMEX-
AIM community is that a methodology is emerging for the systematic explication,
testing, dissemination, and teaching of the heuristic knowledge of medical practice and
scientific performance. Perhaps it is less important that computer programs can be
organized to use this knowledge than that the knowledge itself can be organized for the
use of the human practitioners of today and tomorrow.

Evidence of the impact of SUMEX-AIM in promoting ideas such as these, and
developing the pertinent specific techniques, has been the explosion of interest in
medical artificial intelligence and the specific research efforts of the SUMEX
community. In SUMEX's second decade, we have found that the small community of
researchers that characterized the AIM field in the early 1970's has now grown to a
large, accomplished, and respected research community. The American Association for
Artificial Intelligence (AAAI), the principal scientific membership organization for the
Al field, has 7000 members, over 1000 of whom are members of the medical special
interest group known as the AAAI-M. This subgroup was founded by members of the
SUMEX-AIM community who were active in AAAI and is the only active subgroup in
the Association. The organization distributes semiannual newsletters on medical AI and
provides a focus for cosponsoring relevant medical computing meetings with other
societies (such as the American Association for Medical Systems and Informatics
-- AAMSI). Medical AI papers are prominently featured at both medical computing
and artificial intelligence meetings, and artificial intelligence is now routinely featured
as a specific subtopic for specialized sessions at medical computing and other medical
professional meetings. For example, members of the AIM community have represented
the field to physicians at the American College of Pathology and American College of
Physicians meetings for the last several years. A mere decade ago, the words “artificial
intelligence" were never uttered at such conferences. The growing interest and
recognition are largely due to the activities of the SUMEX-AIM community.

Another indication of the growing impact of the SUMEX-AIM community is its effect
on medical education. For reasons such as those outlined above, there is an increasing
Tecognition of the need for a revolution in the way medicine is taught and medical
students organize and access information. Computing technology is routinely cited as
part of this revolution, and artificial intelligence (and SUMEX-AIM research) generally
figures prominently in such discussions. Such diverse organizations as the National
Library of Medicine, the American Coilege of Physicians, the Association of American
Medical Colleges, and the Medical Library Association have all called for sweeping
changes in medical education, increased educational use of computing technology,
enhanced research in medical computer science, and career development for people
working at the interface between medicine and computing; reports of all four
organizations have specifically cited the role of artificial intelligence techniques in
future medical practice and have used SUMEX-AIM programs as examples of where the
technology is gradually heading.

In summary, the logic which mandates that artificial intelligence play a key role in
enhancing knowledge management and access for biomedicine -- a logic in which we
have long believed -- has gradually become evident to much of the biomedical

E. H. Shortliffe 14
$P41-RR00785-12 Resource Goals and Definitions

community. We are encouraged by this increased recognition, but humbled by the
realization of the significant research challenges that remain. Our goals are accordingly
both scientific and educational. We continue to pursue the research objectives that have
always guided SUMEX-AIM, but must also undertake educational efforts designed to
inform the biomedical community of our results while cautioning it about the
challenges remaining.

15 E. H. Shortliffe
Resource Goals and Definitions $P41-RRO0785-12

Il1.A.2.4. Summary of Current Goals

The following summarizes SUMEX-AIM resource objectives as stated in the proposal
for the on-going five-year grant, begun on August 1, 1981, and provides the backdrop
against which specific progress is reported. These project goals are presented in the
three categories used in the previous proposal: 1) resource operations, 2) training and
education, and 3) core research.

1) Resource Operations

¢ Maintain the vitality of the AIM community by continuing to encourage and
explore new applications of AI to biomedical research and improving
mechanisms for inter- and intra-group collaborations and communications.
User projects will fund their own manpower and local needs; will actively
contribute their special expertise to the SUMEX-AIM community; and will
receive an allocation of computing resources under the control of the AIM
management committees. There, will be no “fee for service" charges for
community members.

¢ Provide effective computational support for AIM community goals, including
efforts to improve the support for artificial intelligence research and new
applications work; to develop new computational tools to support more
Mature projects; and to facilitate testing and research dissemination of nearly
operational programs. We will continue to operate and develop the existing
KI-10/2020 facility as the nucleus of the resource. We will acquire
additional equipment to meet developing community needs for more
capacity, larger program address spaces, and improved interactive facilities,
New computing hardware technologies becoming available now and in the
next few years will play a key role in these developments and we expect to
take the lead in this community for adapting these new tools to biomedical
AI needs. We planned the phased purchase of two VAX computers to
provide increased computing capacity and to support large address space
LISP development, a 2 GByte file server to meet file storage needs, and a
number of single-user “professional workstations” to experiment with
improved human interfaces and AI program dissemination.

e Provide effective and geographically accessible communication facilities to
the SUMEX-AIM community for remote collaborations, communications
among distributed computing nodes, and experimental testing of AI
programs. We will retain the current ARPANET and TYMNET connections
for at least the near term and will actively explore other advantageous
connections to new communications networks and to dedicated links.

2) Training and Education

e Provide community-wide support and work to make resource goals and AI
programs known and available to appropriate medical scientists.
Collaborating projects are responsible for the development and dissemination
of their own AI programs.

e Provide documentation and assistance to interface users to resource facilities
and programs and continue to exploit particular areas of expertise within the
community for developing pilot efforts in new application areas.

« Allocate “collaborative linkage” funds to qualifying new and pilot projects to

E. H. Shortliffe 16
5P41-RR00785-12 Resource Goals and Definitions

provide for communications and terminal support pending formal approval
and funding of their projects. These funds are allocated in cooperation with
the AIM Executive Committee reviews of prospective user projects.

e Support workshop activities, including collaboration with the Rutgers
Computers in Biomedicine resource on the AIM community workshop and
with individual projects for more specialized workshops covering specific
application areas or program dissemination.

3) Core Research

¢ Explore basic artificial intelligence research issues and techniques, including
knowledge acquisition, representation, and utilization; reasoning in the
presence of uncertainty; strategy planning; and explanations of reasoning
pathways, with particular emphasis on biomedical applications.

e Support community efforts to organize and generalize AI tools that have
been developed in the context of individual application projects. This will
include work to organize the present state-of-the-art in AI techniques
through the AI Handbook effort and the development of practical software
packages (e.g., AGE, EMYCIN, UNITS, and EXPERT) for the acquisition,
representation, and utilization of knowledge in AI programs.

17 E. H. Shortliffe
Details of Technical Progress 5§P41-RR00785-12

III.A.3. Details of Technical Progress

This progress summary covers only the resource nucleus. Objectives and progress for
individual collaborating projects are discussed in their respective reports in Section IV.
These collaborative projects collectively provide much of the scientific basis for
SUMEX as a resource and our role in assisting them has been a continuation of that
evolved in the past. Collaborating projects are autonomous in their management and
provide their own manpower and expertise for the development and dissemination of
their AI programs.

III.A.3.1. Progress Highlights

In this section we summarize highlights of SUMEX-AIM resource activities over the
past 4 years, focusing on the resource nucleus.

¢ We have continued to recruit new user projects and collaborators to explore
further biomedical areas for applying AI. A number of these projects are
built around the communications network facilities we have assembled,
bringing together medical and computer science collaborators from remote
institutions and making their research programs available to still other
remote users. At the same time we have encouraged older mature projects to
build their own computing environments thereby freeing up SUMEX
resources for newer projects. Nine projects now operate on their own
facilities, including three that have become BRTP resources in their own
right. Nine projects in the community have completed their research goals
and their staffs have moved on to new areas.

« SUMEX user projects have made good progress in developing and
disseminating effective consultative computer programs for biomedical
research. These performance programs provide expertise in analytical
biochemical analyses and syntheses, clinical diagnosis and decision-making,
molecular biology, and various kinds of cognitive and affective psychological
modeling. We have worked hard to meet their needs and are grateful for
their expressed appreciation (see Section IV).

« We have made significant strategic improvements to the SUMEX-AIM
computing environment in order to optimize computing support for the
community. These developed in ways somewhat different from the initially
projected plan. The DEC VAX computer did not prove to be an effective
machine for running Lisp [23], while Lisp workstations have in fact become
available from a number of vendors as tentatively expected at the time of
our proposal (first Xerox, then Symbolics and LMI, and more recently
Hewlett-Packard and Texas Instruments). Thus, rather than augmenting our
mainframe resources with the purchase of large address space VAX's, we
upgraded the KI-TENEX system to a DEC 2060 and at the same time, began
moving aggressively toward a Lisp workstation-based research environment,
with the approval of an ad hoc site visit group. We did secure VAX
capabilities for our community by means of access to an 11/780 purchased
under DARPA funding. We made an initial purchase of Xerox Dolphins
with NIH funding and subsequently added more Xerox and Symbolics
machines with NIH and DARPA funding and with industrial gifts. Because
of the broad mix of research in the SUMEX-AIM community, no single
workstation vendor can meet our needs so we have undertaken long-term
support of a heterogeneous computing environment, incorporating many
types of machines linked through multiprotocol Ethernet facilities.

E. H. Shortliffe 18
5P41-RR00785-12 Details of Technical Progress

e We have continued the dissemination of SUMEX-AIM technology through
various media. We have distributed various AI software tools to many
research laboratories, including over 200 combined copies of the GENET,
EMYCIN, AGE, MRS, SACON, GLISP, and BB-1 systems. Several of our
software systems have been adapted as commercial AI tools such as the
Teknowledge S.1 and M.1 systems derived from EMYCIN, the Texas
Instruments Personal Consultant system derived from EMYCIN, and the
IntelliCorp KEE system derived from UNITS. We have also prepared video
tapes of some of our research projects including ONCOCIN and an overview
tape of Knowledge Systems Laboratory work.

e Our group has continued to publish actively on the results of our research
including more than 45 research papers per year in the AI literature and a
dozen books in the past 5 years on various aspects of SUMEX-AIM AI
research (see page 81). These books have included the three-volume set of
the Handbook of Artificial Intelligence, edited by Barr, Cohen, and
Feigenbaum; a book on Readings in Medical Artificial Intelligence: The
First Decade by Clancey and Shortliffe; and a book on Rule-Based Expert
Systems: The MYCIN Experiments of the Stanford Heuristic Programming
Project by Buchanan and Shortliffe.

e We completed the GENET project, begun in 1980 as a collaboration between
the MOLGEN investigators and SUMEX, to make a set of DNA sequence
analysis computing tools available to a national community of molecular
biologists. This was an experiment in using a SUMEX-like resource to
disseminate sophisticated software tools to a computer-naive community and
proved extremely successful. GENET served over 300 molecular biologists
before being phased out in early 1983. Subsequently, a new resource called
BIONET has been funded by NIH at IntelliCorp to provide routine service
of the type pioneered by SUMEX/GENET.

e A program in Medical Information Sciences was begun at Stanford in 1983
under Professor Shortliffe as Director. A group of faculty from the Medical
School and the Computer Science Department argued that research in
medical computing has historically been constrained by a lack of talented
individuals who have a solid footing in both the medical and computer
science fields. The specialized curriculum offered by the new program is
intended to overcome the limitations of previous training options. It
focusses on the development of a new generation of researchers with a
commitment to developing new knowledge about optimal methods for
developing practical computer-based solutions to biomedical needs. The
feasibility of this program resulted in large part from the prior work and
research computing environment provided by the SUMEX-AIM resource.
Over 20 PhD and MS trainees will be enrolled in the fall of 1985. It has
been awarded post-doctoral training support from the National Library of
Medicine, received an equipment gift from Hewlett-Packard, and has
received additional industrial and foundation grants for student support.

e We made significant progress in core AI research. In the area of knowledge
representation, work was done on the representation of explicit strategy
knowledge, temporal knowledge, causal knowledge, and knowledge in logic-
based systems. In the area of architectures and control, we worked on a new
implementation of a blackboard architecture with explicit control knowledge.
Under knowledge acquisition studies, three PhD theses were completed
covering experiments in learning by induction, by analogy, and learning
from partial theories. In the area of knowledge utilization, results include

19 E. H. Shortliffe
Details of Technical Progress 5P41-RRO00785-12

work on reasoning with uncertainty and using counterfactual conditionals.
We continued work on a number of existing tools for expert systems and on
building new ones such as the BB1 system. And finally, significant work
was done on the inference of user models, skeletal planning, defining a
taxonomy of diagnostic methods, and reasoning with causal models.

e We have continued the core development of the SUMEX facility hardware,
software, and networking systems to enhance the facilities available to
researchers. Much of this work has centered on the effective integration of
distributed computing resources in the form of mainframes, workstations,
and servers. Network gateways and terminal interface machines based on
MC-68000 microprocessors were developed to link our environment together
and are now the standard system used in the campus-wide Stanford
University network. We developed a gateway interface between Apple
equipment (e.g., the Macintosh and Lisa) and EtherNet hosts that is now in
wide use at universities around the country. We have developed many other
software packages to enhance the computing environments of the Lisp
workstations and to link them to other hosts and servers on our networks.

E. H. Shortliffe 20
5P41-RR00785-12 Details of Technical Progress

III.A.3.2. Resource Equipment Details

The SUMEX-AIM core facility, started in March 1974, was built around a Digital
Equipment Corporation (DEC) KI-10 computer and the TENEX Operating system
which was extended locally to support a dual processor configuration. Because of the
operational load on the KI-10's, in the late 1970's, we had added a small DEC 2020
system (see Figure 2) to support more dedicated testing of systems like ONCOCIN and
Caduceus and for community demos. This facility provided a superb base for the AI
mission of SUMEX-AIM through 1982. Its interactive computing environment, its AI
program development tools, and its network and interpersonal communication media
were unsurpassed in other machine environments. Biomedical scientists found SUMEX
easy to use in exploring applications of developing artificial intelligence programs for
their own work and in stimulating more effective scientific exchanges with colleagues
across the country. Coupled through wide-reaching network facilities, these tools also
give us access to a large computer science research community, including active
artificial intelligence and system development research groups.

The Heterogeneous Computing Environment

In the renewal for the current grant period, both an augmentation of the central
resource in terms of address space and capacity and exploratory work with Lisp
workstations were planned. The Initial Review Group recognized in their special study
Section report the importance of optimizing the timing of our planned hardware
acquisitions to coordinate community needs with the availability of important
technological developments in vendor-supported systems. They recommended in their
Teport that we be allowed considerable flexibility as to phasing of equipment purchases
within the S~year renewal period.

We had initially planned to purchase a large VAX in 1981 and later, our first Lisp
workstations. However, we speeded our push toward workstations for several reasons.
The state of VAX Lisp implementations and projections of their performance were very
discouraging (a study of the VAX InterLisp implementation was done at the time as
documented in [23]). And the first Xerox InterLisp Dolphin workstations were
available for delivery after the summer of 1981. These machines were the prototypes
on which research toward adapting expert AI systems for the interactive workstation
environment could begin. So, we purchased 5 Dolphins for the fall of 1981 and, in
order not to delay non-Lisp SUMEX-AIM work involving VAX machines, we were able
to arrange shared access to a VAX 11/780 funded by ARPA to support Heuristic
Programming Project research. One of the Dolphins we purchased was loaned for
several years to the Rutgers Computers in Biomedicine resource for experimental work.

We continued to evaluate strategies and alternatives for planned system configuration
development. In particular, we had a chance to gain experience with the Dolphin
InterLisp machines and the shared VAX, reassess the role of the dual KI-TENEX
system, and reach a consensus about what the long term configuration of the SUMEX-
AIM facility should be. This was validated by an ad hoc study section review in 1982.
In summary, it was decided that the best resource configuration for the coming decade
would be a shared central machine coupled through a high-performance network to
growing clusters of personal workstations. The central machine should be an extended
addressing TOPS-20 machine and the workstations will be chosen from the viable
products available and scheduled for announcement.

The concept of the individual workstation, especially with the high-bandwidth graphics
interface, proved ideal. Both program development tools and facilities for expert
system user interactions were substantially improved over what is possible with a central
time-shared system. The main shortcomings of these systems were their processing

21 E. H. Shortliffe
Details of Technical Progress $P41-RR00785-12

speed and cost, but the prospect of other workstations to be available from Xerox,
Symbolics, LMI, HP, and others reassured us that these were the right choices for AI
system in the long term. Still, at the time, it was not possible to equip very much of
the SUMEX-AIM community with individual workstations.

Upgrade of the KI-10's to a 2060

Meanwhile, on the mainframe front, given the continued need for a central machine,
the poor Lisp performance of the VAX, and the increasingly untenable difficulties in
maintaining the KI-TENEX system, we decided it is time to retire the KI-10’s and
upgrade them to the then (1982) more modern DEC 2060 TOPS-20 system. This would
free our systems staff to concentrate on more productive development efforts for the
community such as work related to professional workstations and compatible Lisp
support. The 2060 had a processing capacity of 2-3 times that of the dual KI-TENEX
system, badly needed for our community, and it was more compact, reliable, and
maintainable. Pending the arrival of more cost-effective and generally-available Lisp
workstations, this would allow us to continue support for the SUMEX-AIM community
at large and to provide facilities for new AI efforts.

In late 1982, we implemented the upgrade. The purchase price of the DECsystem 2060
reflected a substantial price reduction based on an external research grant from Digital
Equipment Corporation to the Heuristic Programming Project in exchange for access by
DEC to the AI software systems and knowledge-based systems expertise developed by
the HPP. The remainder of the system was funded jointly by NIH and DARPA. The
system configuration is shown in Figure 1. Of course, the transfer of service required a
substantial investment of hardware engineering effort as all of the local line and
network connections had to be changed over. This was all effected invisibly to the user
community by running the old KI-TENEX and the new 2060 systems in parallel for
more than a month.

Using DARPA funding, we also made some upgrades to the shared VAX 11/780 which
was initially purchased by ARPA for HPP research as well as work in network graphics
and VLSI design. The configuration of this machine is shown in Figure 3. In 1983,
we augmented the machine by adding 2 Mbytes of memory and expanding the file
system with a DEC RPO7 disk drive (512 Mbytes). Approximately 60% of the machine
is allocated for HPP and SUMEX use.

The overall facility model then became the central shared 2060, 2020, and VAX 11/780
systems surrounded with growing numbers of workstations and intercoupled by a local
area network.

Additional Workstations

After the purchase of the 5 experimental Dolphin workstations, much work went into
their development by Xerox, based on feedback and interactions with groups such as
ours using them for AI applications. Performance of the Dolphins improved
substantially based largely on improved microcoding of frequently used primitives and
facilities. The initial optimizations of the Dolphin microcode were based on work at
Xerox observing their own programs running. When the Dolphin was exposed to other
AI systems such as ours, it became clear that additional improvements were necessary
and were implemented, including enhanced performance for CONS operations, function
calls, disk management, garbage collection, and other areas. Improvements in individual
areas of performance ranged from factors of 2 to 10.

By 1983, other contenders were entering the Lisp workstation market in addition to
Xerox. Because work in the HPP and the SUMEX-AIM community draws heavily on
both Interlisp and the derivatives of MIT’s MacLisp, we broadened our workstation
experiments into both areas.

E. H. Shortliffe 22
5P41-RR00785-12 Details of Technical Progress

With NIH funding in 1983, we purchased 6 Xerox 1108 workstations (Dandelions) and
in 1984, 3 Xerox 1109's (DandeTigers). With DARPA funding we purchased 2 Xerox
1108's and 1 1132 (high-performance Dorado) in 1984. In early 1985, the ONCOCIN
group received a grant from Xerox of 13 1108's and additional printing and file server
equipment. These machines represent the second generation of Xerox Lisp workstations
and include significantly higher performance and functionality.

With DARPA funding in 1983 we bought a Symbolics LM-2 Tunning the ZetaLisp
system. In 1984, we added 3 Symbolics 3600's and a 3670 and in early 1985, another
3670 -- all with DARPA funding. We are also planning the purchase of additional
workstations in the near term with DARPA funding.

Local Area Network Server Hardware

Since the late 1970's, we have been developing a local, high-speed Ethernet environment
to provide a flexible basis for planned facility developments and the interconnection of
a heterogeneous hardware environment. Our development of Ethernet facilities has
been guided by the goals of providing the most effective range of services for SUMEX
community needs while remaining compatible with and able to contribute to and draw
upon network developments by other groups, dating back to the early 3 Mbit/sec
Ethernet given to Stanford and several other universities by Xerox. We now support
both 3 and 10 Mbit/sec Ethernets (see Figure 5) running numerous protocols and
extended geographically throughout the SUMEX-AIM and related Stanford research
groups. This network is the "glue" that holds the rest of the computing environment
together and consists of numerous servers such as gateways and servers for terminal
access, file storage and retrieval, and laser printing.

In the early phases, a substantial amount of special hardware was developed by our
group for network interfaces including a high-performance direct memory access
interface for the dual KI-TENEX system and a serial phase decoded UNIBUS interface
that are used on our DEC 2020, VAX's, and early PDP-11 gateways and TIP's. The KI
Ethernet interface served well for a period until we upgraded the system to a 2060, at
which time we installed the 2060 mass bus EtherNet interface designed and built by the
Stanford Computer Science Department. Our KI-10 interface is still Seeing service in
connecting another KI-10 system (Institute for Mathematical Studies in the Social
Sciences) to the net.

Hardware for Gateways and TIP’s

As we evolved a more complex network topology and decided to compartmentalize the
overall Stanford internet to avoid electrical interactions during development and to
facilitate different administrative conventions for the use of the various networks, we
developed gateways to couple subnetworks together. These first used PDP-11/05
hardware and then Motorola MC-68000 systems as they became available.

Similarly, we designed gateway between Apple equipment such as the new Macintosh
terminal, that may play a role in our future virtual graphics work, and EtherNet using a
MC-68000 gateway and a locally-designed Apple Bus to Multibus EtherNet interface.
This system incorporates an 8530 Zilog chip to communicate with the Apple Net and
software to manage the protocol packaging.

We also developed a MC-68000 terminal interface processor (TIP) to provide terminal
access to network hosts and facilities. It is basically a machine that has a number of
terminal lines and a network interface and software to manage the establishment of
connections for each line and the flow of characters between the terminal and host. It
can handle up to 32 lines. Both of these systems are now widely used throughout the
Stanford network.

23 E. H. Shortliffe
Details of Technical Progress 5P41-RRO0785-12

File Server Hardware

The development of an EtherNet file server was an integral part of our council-
approved equipment plan with further expansions approved for later years. With joint
NIH and DARPA funding, we were able to take advantage of an exceptional offer by
Digital Equipment Corporation, through their corporate external research sponsorship
program to DARPA contractors (the HPP), to purchase two VAX 11/750 machines as
the processor part of our file servers. In the initial file server configurations, we also
bought Fujitsu Eagle 450 MByte disks and controllers (one each from Systems Industries
and Emulex) with one 800/1600 BPI tape unit for long term archives, and one 300
Mbyte removable pack drive for cyclic backups.

Other Network Hardware

We have developed numerous local network connection systems that have taken
advantage of existing cabling rather than invest in expensive trenching and recabling.
For example, in The Heuristic Programing Project (HPP) move to 701 Welch road, a
high-performance network link to other SUMEX and campus network facilities was
essential. Several communication schemes for establishing a reliable and relatively fast
link were considered, including microwave, infrared laser, direct ethernet (by trenching
and placing a direct ethernet cable), telephone company T1 service and others. All of
these would have involved high cost and so we developed a communication link using
bare copper telephone pair already in place. The wire distance between the HPP Welch
Road location and the SUMEX machine room in the Medical Center is approximately
2000 ft. Utilizing high capacity differential drivers and ultra high speed, high
sensitivity receivers, a half-duplex transceiver was developed for plain copper twisted
pair that achieved error-free transmission at 1.25 Mbits/sec in each direction, utilizing
Manchester data encoding. This communication link has been in operation for well
on a year now without any appreciable down time or noticeable error rate or data
elays.

In addition to the normal continuous flow of maintenance problems, we have
reconnected the very reliable line printer from the old KI-TENEX system to the 2060.
This required substantial modification of the printer controller to adapt to the different
2060 bus signal standards. We have also installed lots of communications equipment,
including dial-in and -out modems and laser printer connections.

E. H. Shortliffe 24
5P41-RRO00785-12

 

Central Processor
DEC KL10-E
2M words of memory, Cache

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Details of Technical Progress

 

PN

 

 

 

 

 

 

 

 

RH20 RH20 RH20 11/40 FRONT END dIiB20
MassBus MassBus Mass8us UNIBUS 140 bus
Disk Controiier Disk Controtier DEC AN20
and Onve and Onve jj ARPAnat
DEC APO? DEC RPos Imertace
Oisk Controiier wes P— O&C BA10
and Orive uss 3 Mbit
Ethernet
DEC RPO7 Intertace
DEC LP10
Line Printer
2 DEC TU-7a , Co TTY
Tape Orives
and Controier
KLINIK Line
Logging TTY
DEC LP.26
Line Printer
—— 6 Line Scanners
— DEC DH-11
96 Lines total
6 Lines a
TYMNET 4. a
me Re

Figure 1:

25

SUMEX-AIM DEC 2060 Configuration

E. H. Shortliffe