Keywords
Artificial intelligence - machine learning - decision-support systems - history of
artificial intelligence in medicine
1 Introduction
The headlines abound: “AI in healthcare market to grow to US$36.1B by 2025” [1], “6 ways AI is changing healthcare” [2], “Artificial Intelligence Transforms the Future of Medicine” [3], “AI Shows Promise Assisting Physicians” [4]. A drive up California’s Bayshore Freeway to San Francisco currently reveals multiple
billboards for Silicon Valley commuters, many touting AI applications and tools from
major corporations and startups (many of which are health-related), while others indicate
the high-paying job opportunities for people with training in AI and data analytics.
We talk to our smart phones and to virtual assistants in our homes and cars, while
radiologists dictate into computer systems that generate text reports automatically.
Futurists and medical pundits envision a new world of “high performance medicine”,
resulting from the convergence of human and artificial intelligence [1].
For those of us who have been engaged with research on artificial intelligence in
medicine and health care for decades, the current visibility and enthusiasm regarding
our field is both refreshing and frightening. It is refreshing for those who worked
hard to create systems that would demonstrate the promise of the field – often with
pushback from the medical community, who could not initially envision the use of computers
in health care that has gradually become commonplace. But it is frightening for those
of us who remember the overselling of AI in general, including medical AI, in the
early 1970s, late 1980s, and early 1990s. The hype in the press in the 1980s led first
to societal curiosity, then to unfounded predictions by observers, and ultimately
to the phenomenon of “AI winter” that followed when predictions proved to be overly
optimistic. With a drop in research funding for anything related explicitly to AI,
there was a tendency for those working in the field to select new terms for the science
they were pursuing. Knowledge representation researchers developed ontologies and the machine learning community gathered at meetings called knowledge discovery in databases (KDD).
At the 1991 AI in Medicine Europe (AIME) meeting, the organizers asked me to present an assessment of the current state
of AI in Medicine (often shortened to AIM). I characterized the field as being in
its adolescence, with some solid accomplishments but many challenges, coupled with
misunderstandings about the nature and goals of the field [2]. Subsequently, at the 2007 AIME meeting, several of us convened an invitational
panel to consider progress in the field in the intervening years, later writing up
our comments to convey our sense that AIM was coming of age [3]. Although we acknowledged the unfortunate impact of the overselling of AIM in earlier
years, we made the case that the field was actually healthy, growing, and poised to
have greater visibility and impact. We pointed to the importance of the increasingly
available computational power, ubiquity of computing, networking and interconnectivity,
and an environment for health professionals and the public that was more accepting
of the notion that computers could play an increasingly valuable support role in health
and health care.
In this article, I will offer a Part 3 to this series of papers, observing how another
decade has affected our field, addressing the challenges in promoting what is possible
while attempting to rein in unrealistic expectations, and anticipating what lies ahead,
both for AIM and for the biomedical informatics discipline to which it contributes.
I begin with a brief recapitulation of the history of AIM, noting that we provided
more detail in those two earlier papers [2]
[3]. I also identify key issues that characterize AIM research as we enter the next
decade in the evolution of medical AI.
2 The Beginnings of AI and its Relevance to Medicine
2 The Beginnings of AI and its Relevance to Medicine
The term “artificial intelligence” was coined by John McCarthy in a proposal for a
computer science conference that was held at Dartmouth College in 1956[5]. McCarthy was then at the Massachusetts Institute of Technology (MIT) but went on
to spend most of his career on the computer science faculty at Stanford University.
A web site maintained by the Association for the Advancement of Artificial Intelligence
(AAAI) nicely summarizes chronologically the subsequent history of the AI field[6]. In the years that followed that conference, several universities developed research
programs in AI, often with support from the Defense Advanced Research Projects Agency
(DARPA) in the US Department of Defense. Early centers of excellence emerged at MIT,
Stanford, and Carnegie Mellon University, and there were important contributions underway
at several other universities as well. The phrase “machine intelligence” was used
in the UK, where there were also important research activities. Although much of the
early work focused on generalized methods for simulating or reproducing human-like
intelligence, by the late 1960s major efforts began to examine how AI methods could
be used to tackle specific problem areas in the natural sciences. At Stanford, the
DENDRAL Project of geneticist Joshua Lederberg and computer scientist Edward Feigenbaum,
in collaboration with organic chemist Carl Djerassi and philosopher-of-science Bruce
Buchanan, used production rules to encode the knowledge of expert chemists. They also
applied clever algorithms that inferred likely chemical structures of organic compounds
from mass-spectral data [4].
Lederberg subsequently led the effort to obtain funding from the National Institutes
of Health (NIH) for a shared computer system at Stanford that was to be connected
to the still-nascent ARPAnet (predecessor to today’s Internet) and would provide a
resource for a community of individuals undertaking research in the application of
AI in the biomedical and clinical sciences. The resulting SUMEX-AIM computer, and
a second networked shared resource at Rutgers University, coordinated to offer services
to AIM researchers, initially within the US and later internationally. Regular national
AIM workshops facilitated collaborations and the formation of a community that came
to know each other’s work well. Much of the AIM community’s work was summarized in
a 1978 special issue of the journal Artificial Intelligence [5], in a 1984 book on major early research projects [6], and in a 1980 NIH publication describing federal investments in the area [7]. My own dissertation work on the MYCIN System [8]
[9] would have been impossible without Lederberg’s leadership [10], Buchanan’s guidance, and the creation of the SUMEX-AIM resource on which I carried
out my research.
Medical AI projects were responsible in part for the explosion of public interest
in expert systems by the early 1980s. New computing machines were developed to support
such work, largely built to optimize the LISP programming language. Major companies
formed AI research groups and built academic collaborations to explore how the new
technologies might stimulate innovation in their businesses. National and international
news outlets ran front-page stories highlighting AI and expert systems, often predicting
rapid changes that would revolutionize society, with health care often a central focus
of the articles. As the decade progressed, however, it became clear that the aspirations
and predictions were not yet achieved, with progress that was slow and projects that
were constrained by the complexity of what was being undertaken. This disillusionment
led to articles about “the failure of AI and expert systems” (AI winter), tainting
the reputation of the field even as slow and steady progress continued to be made[7].
By the time of the previously mentioned AIME meeting in Maastricht (1991), the global
AIM research community was active and vibrant, but somewhat sidelined and reluctant
to characterize what they were doing as AI or expert systems research. Introspection
on an international AIM list server led to my summary of, and responses to, key questions
being asked in the community:
-
Have we been so focused on decision-making performance that we have failed to address
user needs? [Response: The best work had not failed to consider these issues.]
-
Can we identify clear contributions of AIM to the general discipline of AI, to psychology,
to clinical medicine? [Response: Yes, for the first two, but impact on clinical medicine was still to be
achieved.]
-
Should we view AIM as part of information systems, computer science, AI itself, engineering,
or biomedicine? [Response: None of the above. It is a key component of biomedical informatics, which
is itself a separate hybrid discipline.]
-
Are we training AIM scientists well? [Response: Only in a few places at that time.]
-
Is there inbreeding or a limitation of perspective? [Response: Should not be a problem as the field grows and prospers, which has subsequently
been borne out.]
-
Why does there appear to be a lack of studies of AIM systems in routine use? [Response: The problems are difficult, systems can adversely affect workflow, and
it is especially hard to introduce systems smoothly in the absence of standards that
support integration.]
-
Given the potential importance of the field, why is it poorly funded by research agencies?
[Response: Getting funding can always be challenging, and AI had come into some disfavor,
but excellent work was still being supported.]
-
Why are our systems exceedingly difficult to move from one site to another? [Response: Many reasons, including lack of sensitivity to cultural and process differences
across institutions, absence of standard terminologies or connectivity standards,
and “not invented here” biases.]
These questions, and my more detailed answers to them, became the focus of my 1991
AIME presentation and of the subsequent published version to which readers may refer
for a then-current (and generally upbeat) analysis [2].
The next 15 years saw remarkable changes both in technology and in the capabilities
of AIM systems. Machine learning had become a significant focus of AIM research [11]
[12], and the importance of integration with clinical systems (facilitated by the gradual
development of pertinent standards) had become well recognized. The biological sciences
had embraced AI notions, spurred on by the size of their datasets and the success
of the Human Genome Project in the 1990s. Meanwhile, wireless networking, smart phones,
and social media had all contributed to a societal acceptance of computing that had
been less prevalent even a decade earlier. When we gathered for the previously mentioned
AIME session in Amsterdam (2007), organizers asked the five panelists to address how
the field had advanced since 1991. They also wanted to know the extent to which it
was influencing biomedicine or clinical fields, and how well it was being supported
by funding agencies, by academic or research entities, and by colleagues in computer
science or biomedical fields. Those perspectives are summarized in what I have called
Part 2 in this series of papers [3]. In my own comments I stressed that AIM could not be set off from the rest of biomedical
informatics (as it sometimes had been), and that it was also highly pertinent to the
world of health planning and policy. But a major point was my emphasis on the importance
of training a cadre of individuals who are deeply skilled in computer science and
AI, but also knowledgeable about, and comfortable in, the life science research laboratory
and the world of clinical medicine and practice.
3 Evolution of AIM in the Last Decade
3 Evolution of AIM in the Last Decade
A half-century has passed since the earliest explorations of AI in the life sciences
and medicine. It is accordingly not surprising that a great deal has changed since
the earliest notions were formed regarding the field and its potential. For example,
there was an early debate as to whether AI should attempt to understand and simulate
human reasoning processes, using methods such as formal logic that were provably correct.
Those who extolled this view were dubbed neats, whereas the scruffies were those who were more concerned with human-like performance, regardless of whether
the underlying methods were elegant or were homogenous in the way that they represented
and demonstrated the notion of intelligence[8]. Although this distinction continues to exist among AI researchers, the scruffy
approach now dominates, and much of the intelligent performance we observe in computers
today has little to do with the way that human beings would tackle the same problem.
AI has taken on a kind of magical quality, with most people impressed by how smart
computers appear to have become but having little insight into how they achieve what
they do.
Consider, for example, the long history of AI research in natural language processing
and machine learning that led to the 2011 television performance of IBM’s Watson when it won a Jeopardy competition against two human game champions [13]. People had vague notions that Watson knew a lot, and was very fast, but the long
line of research that had led to such performance is not well appreciated. As early
as 1959, Arthur Samuel (at IBM) began his exploration of machine learning that resulted
in a computer that was able to play checkers, to learn how to win by playing thousands
of games against itself after it knew the rules, and eventually to beat human competitors[9]. The perceptron predecessor to neural networks, viewed as a method for recognizing
patterns by simulating brain neuronal interconnectivity, was first explored in the
1950s and later popularized in a 1969 book by Minsky and Papert [14], long before neural networks with multiple “deep” hidden layers would have been
computationally tractable. Research on natural language processing and text mining
also has a long history[10].
We can make similar observations about our everyday acceptance of the speech-understanding
capabilities of Siri or Alexa, both of which owe their performance to decades of AI investigation, such as the
influential Hearsay effort at Carnegie Mellon University in the 1960s and 1970s [15]. And when I first arrived at Stanford University in 1970, I was excited when I visited
Stanford Research Institute (now known as SRI International) to see the performance
of an early wheeled intelligent robot, Shakey, as it solved problems regarding mobility
and performed physical tasks[11]. Later, on my first visit to the Stanford AI Laboratory, nestled on a hill above
the campus, I drove in and was intrigued to see a sign “Caution: Robot Vehicle”. Researchers
had begun a line of research on driverless vehicles that has continued for decades[12] and has contributed to the current notion that our Uber may soon arrive without
a driver in the front seat[13].
I mention these early AI research activities to stress that what we embrace today
did not suddenly appear from a commercial company but was the result of decades of
research, typically with slow progress and a visionary set of scientists and government
funding agencies who advanced the field. People chatting with intelligent agents in
their phones have little insight into what was required to create the technology that
they now take for granted. And in the popular media, the phrase “AI” has transformed
from being the name of a computer-science subfield into a noun used to describe a
sentient but digital being – “there is an AI in my phone!”.
There is, of course, also a medical AI version of this story. It is caught up in a
confusion of terms that have recently come into vogue. Researchers in AIM, and in
biomedical informatics in general, have long struggled with large amounts of data
that have pushed the boundaries of what can reasonably be tackled with existing technology.
Similarly, they have always developed analytical methods for dealing with such data,
combining statistical and computational techniques. Thus “big data” (where “big” is
a relative term, reflecting evolution in computing capacity and technology) have always
characterized our field. We have always embraced the need for “data analytics” and
have developed corresponding methods. However, in recent years, notions of data science have emerged as though they are new and have not always been with us. Enthusiasm
for new deep machine-learning methods and data science has often failed to acknowledge
that those topics are part of a continuum that has been explored by computer scientists,
biomedical informaticians, and AIM researchers for decades. In some circles, machine
learning and artificial intelligence are seen as nearly synonymous, and it is unfortunate
that both academic and government institutions are increasingly creating organizational
structures suggesting that biomedical data science and biomedical informatics are
separate disciplines or activities.
As previously mentioned, AIM is naturally entwined with other areas of computer science
and biomedical informatics. Although there is no chapter on AIM in our textbook of
biomedical informatics [16], the field arises throughout the volume in almost every chapter, e.g., those dealing
with decision science, cognitive science, human-computer interaction, bioinformatics,
natural language processing, decision-support systems, imaging, and the like. As editor
of the Journal of Biomedical Informatics (JBI), I also have found that a very high percentage of current submissions deal
with AIM topics, and especially with NLP and machine learning applied to health care
problems. Many are novel applications of existing techniques[14], but some papers introduce new methods motivated by medical problems, and many of
these generalize broadly for diverse applications, including outside of biomedicine
and health. Similar phenomena are evident when I peruse other informatics journals,
leading to my assertion that AIM-related research is a core element in current informatics
investigative activities.
Extensive AIM research activity is now, for the first time, matched by enthusiasm
for the field[15] and by AI investment by health care organizations and in industry, including new
startup companies. Top medical journals have published editorials or articles extolling
the promise of AI [17]
[18], and the press is filled with articles on the topic[16]. Expectations are high, and the public seems more accepting of the notion that AI
will play a role in the care that they receive.
There are cautionary notes as well. For example, some fear that AI could worsen health
disparities [19], and some argue that AI is simply being overhyped and has a long way to go [20]. Some of us fear another AI winter could set in, but on balance the field is proving
itself with formal studies as well as anecdotal stories.
4 Taking a Deeper Look
Today the clinical literature is replete with formal studies demonstrating the quality
and value of AI methods, especially in the interpretation of large datasets, typically
using deep learning approaches. Perhaps the most visible successes are those dealing
with image interpretation – radiographs [21], ultrasound scans [22], photographs [23], skin lesions [24], microbiology [25], and the like. The results of some studies have been so impressive that there is
even debate in the radiological community about whether radiologists will be replaced
by AI systems[17]. I find this unlikely, but the nature of radiologic work and expertise will evolve.
The use of deep-learning techniques for image interpretation, and their gradual insertion
into routine use, are similar to what the medical world has already accepted in the
area of electrocardiographic (ECG) interpretation. In much of the world, ECG reports
are now generated by computers and then checked and approved by a cardiologist. The
ordering physicians can still review the ECG themselves and decide whether they agree
with the interpretation, but there is broad recognition that it is more efficient,
and generally just as accurate (or more so, given issues of human fatigue and distractibility),
to have a computer perform the initial analysis and to generate the report. The arguments
would seem to be even stronger in the case of radiologic interpretation, where there
is growing evidence that, on average, the AI interpretation will be more accurate.
Of course, as in the ECG example, physician review and approval are likely to be required
for the foreseeable future.
One issue that arises frequently in the setting of deep learning systems is their
lack of transparency: the scoring algorithms cannot easily be translated into explanations
regarding the basis for a program’s decision. Government research agencies realize
that this is an ongoing issue[18], and, given the nature of medicine and the independence of clinicians, explanation
has long been viewed as an important element in an AIM system. For programs that use
machine learning methods to perform signal or image analysis, however, such explanations
may be less pertinent. The ECG example shows that clinicians will accept such systems
if they can independently validate the decision offered by a program.
A colleague and I have recently argued that the situation may be different when an
AIM system is offering diagnostic or therapeutic advice through a direct interaction
with the clinician [26]. We have known for decades that it is especially challenging to motivate clinicians
to use a decision-support tool when they must interact with the program themselves
(and not simply receive a computer-generated report). Although some colleagues have
argued that explanation is not a crucial requirement, now that the use of computers
is more clinically accepted, my own experience would suggest otherwise. I have found
that clinicians use explanation capabilities less frequently as they became comfortable
with an advisory tool, but they want to know that the capability exists in case they
have questions. This observation led me to argue in the recent article [26] that “black boxes are unacceptable” when a decision-support tool is designed for
direct use by a clinician.
The need for explanation was part of the reason why earlier AIM systems emphasized
the “knowledge is power” concept [27], which for decades became a kind of mantra for the expert systems research community.
I believe that the true power of our deep-learning applications will occur when we
have better approaches for merging the analytical power of machine learning with an
explicit encoding of the relevant domain semantics. It is one of the reasons that
my recent article [26] stressed that “relevance and insight are essential”, explaining that a decision
support system “should reflect an understanding of the pertinent domain and the kinds
of questions with which clinicians are likely to want assistance.”
I also believe that we must counter all suggestions that AIM systems are being designed
to make the therapeutic or management decisions. We must stress that it is the synergy
between user and machine, and not the machine alone, that properly leverages the unique
skills of both participants. It is through this kind of partnership that the care
of patients will improve and flourish as AIM systems became routinely integrated into
care settings.
5 Moving Forward
With new companies forming, new products being released, and established firms adding
major AIM elements to their business strategy, one could ask how the AIM research
community might best interface with these new kinds of activities. In the days when
there was almost no commercial interest in medical AI, the work was largely based
in academia, supported as research, and implemented and evaluated in affiliated clinical
settings. Today there is an enhanced potential for partnerships between those in the
AIM research community and those involved with the commercialization of the technology.
On the one hand, there should be opportunities for joint research projects, largely
academically based, in which industry and academic AIM groups combine their unique
skills and resources to develop new methods and systems. There is an existing model
for this kind of collaboration in the computer-science community, where firms financially
sponsor joint research, students, and even professorships. It is time to forge more
such relationships between members of the biomedical informatics academic community
and their counterparts in industry.
But there is another way in which the AIM community, and others in academic biomedical
informatics, can work proactively and collaboratively with industry. We can take an
example from the pharmaceutical industry, which long ago realized that the world of
clinical medicine demands evidence that is rigorously developed and published in peer-reviewed
journals. If such research were carried out by the company itself, there would be
inevitable concerns about conflicts of interest that might taint the study design,
its implementation, or the subsequent analysis. As a result, the pharmaceutical firms
often collaborate with (and provide funding for) academic medical groups and groups
of practitioners who take the lead in the implementation, data gathering, analysis,
and publication of the results.
By analogy, the budding AIM industry needs to take seriously the importance of rigorous
evaluation of their products and thus the importance of collaborations with independent
groups, typically in academia, to carry out the relevant trials. In the case of clinical
systems, this will mean industry helping to fund studies that establish a product’s
safety, validity, reproducibility, usability, and reliability [26]. There is accordingly great synergy that matches the needs and interests of the
AIM industry as it evolves and those of the academic AIM and biomedical informatics
communities.
I close, as I often do, with an appeal for more training of talented individuals to
work in both the academic and industry sides of this AIM research relationship. Due
to a paucity of AIM graduates, too many companies have had to settle for hiring individuals
with computer-science talent but limited understanding of the culture of medicine
or the details of practice settings. The insertion of even one well-trained biomedical
informatics scientist into such settings can radically influence the way a product
develops, evaluations are performed, or corporate strategies are defined. We could
debate the meaning of “well-trained”, but at a minimum I believe it implies solid
technical skills in computer science and AI, coupled with a deep understanding of
medicine and clinical care.
The future of AIM is bright, building on the remarkable transformation in technology,
computing, medicine, and biology over the past half-century. Terminology may change,
and experiments will continue to falter along the way, but consideration of the steady
progress of five decades is inspiring, even if the rate of progress has been slower
than we might have hoped or predicted. We should learn from that experience and avoid
predictions today that are ill-considered or that fail to appreciate the complexity
of the task that we have undertaken and that will continue to challenge us – often
in ways that will surprise us.
