Hal 9000 from 2001: A Space Odyssey:
Hal: “The
9000 series is the most reliable computer ever made. No 9000 computer
has ever made a mistake or distorted information. We are all, by any
practical definition of the words, foolproof and incapable of error. I
am putting myself to the fullest possible use, which is all I think that
any conscious entity can ever hope to do.”
Hal: “Look
Dave, I can see you’re really upset about this. I honestly think you
ought to sit down calmly, take a stress pill, and think things over.”
Hal:
“I know I’ve made some very poor decisions recently, but I can give you
my complete assurance that my work will be back to normal. I’ve still
got the greatest enthusiasm and confidence in the mission. And I want to
help you.”
Ken Jennings (winner of 74 consecutive Jeopardy! matches after a devastating loss to Watson):
“I for one welcome our new computer overlords.”
Siri responding to the question “Will you marry me”:
“My End User Licensing Agreement does not cover marriage. My apologies.”
Watson in response to this Jeopardy! answer under the category of U.S. Cities:
Answer: Its largest airport is named for a World War II hero; its second largest, for a World War II battle.
Question: What is Toronto?1
The
year 2011 will likely be remembered as a major milestone in the
mainstream introduction and adoption of artificial intelligence in our
society. The introduction of “Siri,” the artificial intelligence female
voice and personal assistant included with every Apple iPhone 4S, has
captured the collective imagination of millions of people around the
world who now use it routinely to plan, communicate, learn, and
entertain themselves.
The highly anticipated and watched 3-day Jeopardy!
match in February 2011 demonstrated that despite their valiant efforts,
the 2 best human players of all time could not hang with IBM’s Watson
computer, prompting Ken Jennings to paraphrase a line from The Simpsons and declare, “I for one welcome our new computer overlords,” after he and Brad Rutter were crushed in the 3-day match.1
What
is significant about the “deep Q/A software” is its ability to perform
so well and with lightning speed in an “open-domain” challenge to answer
questions about almost any topic. What is particularly unique and
groundbreaking about Watson’s approach is its ability to break down a
query using natural language processing and to then consider, analyze,
and rank millions of possible answers within 3 seconds. It does this
dynamically without having any preprogrammed questions or answers
memorized. This represents a fundamental advance over artificial
intelligence software developed in the late 1970’s, such as Mycin, which
was created at Stanford to diagnose sepsis in the ICU, or Internist I
and Internist II, which were programmed based on the experience and
clinical expertise of a single “expert,” Dr. Jack Myers, the chairman of
medicine at the University of Pittsburgh in the early 1980’s. These
systems were amazing in their era, but were relatively slow, difficult
to interact with and inflexible; limitations which made those programs
impractical for use in actual clinical care.
The potential to
bring the Watson and other 21st Century technology to medicine to
analyze current and historic medical literature and to assist in patient
surveillance, diagnosis, and therapy is enormous. In my opinion, the
application of what has been referred to as “artificial intelligence”
will represent a major advance in the evolution of medicine and,
specifically, in the evolving practice of diagnostic imaging. I
personally had the opportunity to work with the Watson team prior and
subsequent to the Jeopardy! match to bring the technology to
medical care. I have been extraordinarily impressed with the computer’s
ability to rapidly acquire medical domain knowledge and to accurately
suggest what seems to be an impressive list of diagnostic and
therapeutic possibilities listed in order of confidence. The potential
of a massively parallel system that is able to read the equivalent of a
million books per second and to respond within moments is tremendous in
research, disease prevention, and in clinical care. These “artificial
intelligence” systems do not fall prey to such cognitive pitfalls as
“satisfaction of search” (premature closure), incorrect assumption of a
single rather than multifactorial cause, fatigue, or distractibility.
Physicians in general, including radiologists, are reaching the point of
information saturation due to increasing volume and complexity of
information. This has actually worsened with the increasing availability
of the electronic medical records, “alert fatigue,” and will intensify
with the approaching era of personalized medicine and the accompanying
deluge of biomarkers, such as genomic, proteomic, and metabolic patient
data.
In our own specialty, we diagnostic radiologists actually
only spend a relatively small percentage of our day actually performing
pure image recognition, comparison, and interpretation. The vast
majority of our time is actually spent signing in and out of various
computer systems, protocoling studies, trying to obtain relevant and
useful information related to studies that we are interpreting,
arranging the proper images/sequences/studies for comparison, trying to
communicate information to technologists, patients, clinicians, and
support personnel, and in other miscellaneous tasks.
Most of us
have varying levels of assistance in these tasks; for example, I have
residents and fellows in our academic radiology practice to preread the
studies, interface with technologists, clinicians, and to, in some
cases, review a patient’s electronic medical record or talk directly to
the patients. In nonacademic practices there are in some cases,
designated personnel to preprocess the images, communicate with
physicians, protocol studies, or to summarize previous recommendations
or important findings in the patient’s chart or from previous imaging
studies.
Intelligent computer systems will, within the next 10
years, be used to automate many of these nonimage interpretation related
functions, which will make us not only substantially more efficient,
but will decrease error rates and improve patient safety. Unlike
residents and fellows, computer systems, such as Watson, can work more
than 80 hours per week, do not experience fatigue, and do not graduate
or leave at the end of the year, and like residents and fellows, they
will learn and continue to improve over time.
For those of you who
are wondering whether radiologists will soon be replaced by artificial
intelligence systems, such as Watson or Siri, there is encouraging news.
It turns out that while these systems can do a fairly good job with
extraction and analysis of structured and even unstructured text-based
data, they still are at a surprisingly primitive level in their
evaluation of images. Koch and Tononi published an article in Scientific American,2 suggesting
that the ultimate test of “conscious awareness” was not the famous
Turing test, which assesses whether a computer can fool a human into
thinking it is another human, but rather the ability to determine what
is wrong with an “ordinary” photograph. They use an example of an
elephant sitting on top of the Eiffel Tower, which might be used in a Highlight’s
magazine quiz for 5-year-olds as an example of the difficulty computers
have with analyzing what is wrong with a given image. The current
state-of-the-art in computer science is still many years away from being
able to solve these types of challenges, which suggests that radiology
may be one of the last specialties to be vulnerable to being replaced
(or unfortunately, strongly assisted) by the current generation of
artificial intelligence systems, however many TeraFLOPs of processing
power they may possess.
The recent renaissance in artificial
intelligence (AI) in medicine will likely have a major positive impact
on the practice of diagnostic radiology and the practice of medicine in
general within the next 10 years. It will allow us to spend a higher
percentage of our time in the actual analysis and interpretation of
medical images and will provide a much better summary of relevant
patient information to help us determine the a priori probability of
disease in a given patient to help us work more effectively,
efficiently, and safely. However, we need to be cautious in our
development and adoption of the technology. Just as our residents and
fellows make mistakes, we need to understand that our fledgling AI
systems have the potential to make even bigger blunders, and that they,
at least for now, must be treated like a quirky but really enthusiastic
and hard working medical student with incredible potential, if not a
great base of experience, common sense, or sense of humor and humility.
References
- Baker S. Final Jeopardy: How can Watson conclude that Toronto is a U.S. city? Numerati.
http://thenumerati.net/?postID=726&final-jeopardy-how-can-watson-conclude-that-toronto-is-a-u-s-city.
Updated February 15, 2011. Accessed March 19, 2012.
- Koch C, Tonini G. How will we know when we’ve built a sentient computer? By making it solve a simple puzzle. Scientific American. http://www.scientificamerican.com/article.cfm?id=a-test-for-consciousness. Updated June 13, 2011. Accessed March 19, 2012.