,Chapter 1. Myths and Realities of AI
Pamela McCorduck, in her book Machines Who Think (W. H. Freeman), describes AI as an
“audacious effort to duplicate in an artifact” what all of us see as our most defining attribute:
human intelligence. Her 1979 book provides a fascinating glimpse into early thinking about AI—
not using theorems or science, but instead describing how people came to imagine its possibilities.
With something so magical and awe-inspiring as AI, it’s not hard to imagine the surrounding
hyperbole. This chapter hopes to maintain the awe but ground it in reality.
Stuart Russell, a computer scientist and one of the most important AI thinkers of the 21st century,
discusses the past, present, and future of AI in his book Human Compatible (Viking). Russell
writes that AI is rapidly becoming a pervasive aspect of today and will be the dominant technology
of the future. Perhaps in no industry but healthcare is this so true, and we hope to address the
implications of that in this book.
For most people, the term artificial intelligence evokes a number of attributed properties and
capabilities—some real, many futuristic, and others imagined. AI does have several superpowers,
but it is not a “silver bullet” that will solve skyrocketing healthcare costs and the growing burden
of illness. That said, thoughtful AI use in healthcare creates an enormous opportunity to help
people live healthier lives and, in doing so, control some healthcare costs and drive better outcomes.
This chapter describes healthcare and technology myths surrounding AI as a prelude to discussing
how AI-enhanced apps, systems, processes, and platforms provide enormous advantages in quality,
speed, effectiveness, cost, and capacity, allowing clinicians to focus on people and their healthcare.
A lot of the hype accompanying AI stems from machine learning models’ performance compared
to that of people, often clinicians. Papers and algorithms abound describing machine learning
models outperforming humans in various tasks ranging from image and voice recognition to
language processing and predictions. This raises the question of whether machine learning (ML)
diagnosticians will become the norm. However, the performance of these models in clinical
practice often differs from their performance in the lab; machine learning models built on training
and test data sometimes fail to achieve the same success in areas such as object detection (e.g.,
identifying a tumor) or disease prediction. Real-world data is different—that is, the training data
does not match the real-world data—and this causes a data shift. For example, something as simple
as variation in skin types could cause a model trained in the lab to lose accuracy in a clinical setting.
ML diagnosticians may be our future, but additional innovation must occur for algorithm
diagnosticians to become a reality.
The hyperbole and myths that have emerged around AI blur the art of what’s possible with AI.
Before discussing those myths, let’s understand what we mean by AI. Descriptions of AI are
abundant, but the utility of AI will be more important than a definition. Much of this book will
explore the service of AI. We provide clarity in helping with understanding the context and
meaning of the term AI. A brief look at its origin provides a useful framework for understanding
how AI is understood and used today.
,AI Origins and Definition
Humans imagining the art of what’s possible with artificial life and machines has been centuries
in the making. In her 2018 book Gods and Robots (Princeton University Press), Adrienne Mayor,
a research scholar, paints a picture of humans envisioning artificial life in the early years of
recorded history. She writes about ancient dreams and myths of technology enhancing humans. A
few thousand years later, in 1943, two Chicago-area researchers introduced the notion of neural
networks in a paper describing a mathematical model. The two researchers—a neuroscientist,
Warren S. McCulloch, and a logician, Walter Pitts—attempted to explain how the complex
decision processes of the human brain work using math. This was the birth of neural networks,
and the dawn of artificial intelligence as we now know it.
Decades later, in a small town along the Connecticut River in New Hampshire, a plaque hangs in
Dartmouth Hall, commemorating a 1956 summer research project, a brainstorming session
conducted by mathematicians and scientists. The names of the founding fathers of AI are engraved
on the plaque, recognizing them for their contributions during that summer session, which was the
first time that the words “artificial intelligence” were used; John McCarthy, widely known as the
father of AI, gets credit for coining the term.
The attendees at the Dartmouth summer session imagined artificial intelligence as computers doing
things that we perceive as displays of human intelligence. They discussed ideas ranging from
computers that understand human speech to machines that operate like the human brain, using
neurons. What better display of intelligence than devices that are able to speak and understand
human language, now known as natural language processing? During this summer session, AI
founders drew inspiration from how the human brain works as it relays information between input
receptors, neurons, and deep brain matter. Consequently, thinking emerged on using artificial
neurons as a technique for mimicking the human brain.
Enthusiasm and promises for the transformation of healthcare abound, but this goal remains
elusive. In the 1960s, the AI community introduced expert systems, which attempt to transfer
expertise from an expert (e.g., a doctor) to computers using rules and then to apply them to a
knowledge base to deduce new information, an inference. In the 1970s, rules-based systems like
MYCIN, an AI system engineered to treat blood infections, held much promise. MYCIN attempted
to diagnose patients using their symptoms and clinical test results. Although its results were better
than or comparable to those of specialists in blood infections, its use in clinical practice did not
materialize. Another medical expert system, CADUCEUS, tried to improve on MYCIN. MYCIN,
CADUCEUS, and other expert systems (such as INTERNIST-I) illustrate the efforts of the AI
community to create clinical diagnostic tools, but none of these systems found its way into clinical
practice.
This situation persists today; AI in healthcare is not readily found at the clinical bedside. Several
research papers demonstrate that AI performs better than humans at tasks such as diagnosing
disease. For example, deep learning algorithms outperform radiologists at spotting malignant
tumors. Yet these “superior” disease-detecting algorithms largely remain in the labs. Will these
, machine learning diagnostic tools meet the same fate as the expert systems of the 20th century?
Will it be many years before AI substantially augments humans in clinical settings?
This is not the 1970s; AI now permeates healthcare in a variety of ways—in production, for
example. Artificial intelligence helps researchers in drug creation for various diseases, such as
cancer. Beth Israel Deaconess Medical Center, a teaching hospital of Harvard Medical School,
uses AI to diagnose possible terminal blood diseases. It uses AI-enhanced microscopes to scan for
injurious bacteria like E. coli in blood samples, working at a faster rate than manual
scanning. Natural language processing is widely used for automating the extraction and encoding
of clinical data from physician notes. Multiple tools at work in production settings today use
natural language processing for clinical coding. Machine learning helps with steering patients to
the optimal providers. For decades, machine learning has been used to identify fraud and reduce
waste. The widespread adoption of AI in specific use cases in healthcare companies, coupled with
recent innovations in AI, holds tremendous promise for expanding the use of AI in clinical settings.
This book on AI-First healthcare hopes to show a different future for the widespread adoption of
AI in healthcare, including in clinical settings and in people’s homes. There continues to be an
active conversation among clinicians and technologists about implementing AI in healthcare. A
2019 symposium attended by clinicians, policy makers, healthcare professionals, and computer
scientists profiled real-world examples of AI moving from the lab to the clinic.1 The symposium
highlighted three themes for success: life cycle planning, stakeholder involvement, and
contextualizing AI products and tools in existing workflows.
A lot has changed in the years since the conception of neural networks in 1943. AI continues to
evolve every decade, explaining why consensus on an AI definition remains elusive. Since we are
not all operating with the same definition, there is a lot of confusion around what AI is and what
it is not. How artificial intelligence is defined can depend on who provides the explanation, the
context, and the reason for offering a definition. AI is a broad term representing our intent to build
humanlike intelligent entities for selected tasks. The goal is to use fields of science, mathematics,
and technology to mimic or replicate human intelligence with machines, and we call this “AI.” In
this book, we will explore several intelligent entities, such as augmented doctors, prediction
machines, virtual care spaces, and more, that improve healthcare outcomes, patient care,
experiences, and cost.
We continue to build and exhibit systems, machines, and computers that can do what we previously
had understood only humans could do: win at checkers, beat a reigning world chess master, best
the winningest Jeopardy! contestants. Famously, the computer program AlphaGo has defeated
world champions in the 4,000-year-old abstract strategy game Go and excels in emulating (and
surpassing) human performance of this game. Articles abound on machine learning models in labs
outperforming doctors on selected tasks, such as identification of possible cancerous tumors in
imaging studies; this suggests that AI may eventually replace some physician specialties, such as
radiologist.
AI and Machine Learning
Pamela McCorduck, in her book Machines Who Think (W. H. Freeman), describes AI as an
“audacious effort to duplicate in an artifact” what all of us see as our most defining attribute:
human intelligence. Her 1979 book provides a fascinating glimpse into early thinking about AI—
not using theorems or science, but instead describing how people came to imagine its possibilities.
With something so magical and awe-inspiring as AI, it’s not hard to imagine the surrounding
hyperbole. This chapter hopes to maintain the awe but ground it in reality.
Stuart Russell, a computer scientist and one of the most important AI thinkers of the 21st century,
discusses the past, present, and future of AI in his book Human Compatible (Viking). Russell
writes that AI is rapidly becoming a pervasive aspect of today and will be the dominant technology
of the future. Perhaps in no industry but healthcare is this so true, and we hope to address the
implications of that in this book.
For most people, the term artificial intelligence evokes a number of attributed properties and
capabilities—some real, many futuristic, and others imagined. AI does have several superpowers,
but it is not a “silver bullet” that will solve skyrocketing healthcare costs and the growing burden
of illness. That said, thoughtful AI use in healthcare creates an enormous opportunity to help
people live healthier lives and, in doing so, control some healthcare costs and drive better outcomes.
This chapter describes healthcare and technology myths surrounding AI as a prelude to discussing
how AI-enhanced apps, systems, processes, and platforms provide enormous advantages in quality,
speed, effectiveness, cost, and capacity, allowing clinicians to focus on people and their healthcare.
A lot of the hype accompanying AI stems from machine learning models’ performance compared
to that of people, often clinicians. Papers and algorithms abound describing machine learning
models outperforming humans in various tasks ranging from image and voice recognition to
language processing and predictions. This raises the question of whether machine learning (ML)
diagnosticians will become the norm. However, the performance of these models in clinical
practice often differs from their performance in the lab; machine learning models built on training
and test data sometimes fail to achieve the same success in areas such as object detection (e.g.,
identifying a tumor) or disease prediction. Real-world data is different—that is, the training data
does not match the real-world data—and this causes a data shift. For example, something as simple
as variation in skin types could cause a model trained in the lab to lose accuracy in a clinical setting.
ML diagnosticians may be our future, but additional innovation must occur for algorithm
diagnosticians to become a reality.
The hyperbole and myths that have emerged around AI blur the art of what’s possible with AI.
Before discussing those myths, let’s understand what we mean by AI. Descriptions of AI are
abundant, but the utility of AI will be more important than a definition. Much of this book will
explore the service of AI. We provide clarity in helping with understanding the context and
meaning of the term AI. A brief look at its origin provides a useful framework for understanding
how AI is understood and used today.
,AI Origins and Definition
Humans imagining the art of what’s possible with artificial life and machines has been centuries
in the making. In her 2018 book Gods and Robots (Princeton University Press), Adrienne Mayor,
a research scholar, paints a picture of humans envisioning artificial life in the early years of
recorded history. She writes about ancient dreams and myths of technology enhancing humans. A
few thousand years later, in 1943, two Chicago-area researchers introduced the notion of neural
networks in a paper describing a mathematical model. The two researchers—a neuroscientist,
Warren S. McCulloch, and a logician, Walter Pitts—attempted to explain how the complex
decision processes of the human brain work using math. This was the birth of neural networks,
and the dawn of artificial intelligence as we now know it.
Decades later, in a small town along the Connecticut River in New Hampshire, a plaque hangs in
Dartmouth Hall, commemorating a 1956 summer research project, a brainstorming session
conducted by mathematicians and scientists. The names of the founding fathers of AI are engraved
on the plaque, recognizing them for their contributions during that summer session, which was the
first time that the words “artificial intelligence” were used; John McCarthy, widely known as the
father of AI, gets credit for coining the term.
The attendees at the Dartmouth summer session imagined artificial intelligence as computers doing
things that we perceive as displays of human intelligence. They discussed ideas ranging from
computers that understand human speech to machines that operate like the human brain, using
neurons. What better display of intelligence than devices that are able to speak and understand
human language, now known as natural language processing? During this summer session, AI
founders drew inspiration from how the human brain works as it relays information between input
receptors, neurons, and deep brain matter. Consequently, thinking emerged on using artificial
neurons as a technique for mimicking the human brain.
Enthusiasm and promises for the transformation of healthcare abound, but this goal remains
elusive. In the 1960s, the AI community introduced expert systems, which attempt to transfer
expertise from an expert (e.g., a doctor) to computers using rules and then to apply them to a
knowledge base to deduce new information, an inference. In the 1970s, rules-based systems like
MYCIN, an AI system engineered to treat blood infections, held much promise. MYCIN attempted
to diagnose patients using their symptoms and clinical test results. Although its results were better
than or comparable to those of specialists in blood infections, its use in clinical practice did not
materialize. Another medical expert system, CADUCEUS, tried to improve on MYCIN. MYCIN,
CADUCEUS, and other expert systems (such as INTERNIST-I) illustrate the efforts of the AI
community to create clinical diagnostic tools, but none of these systems found its way into clinical
practice.
This situation persists today; AI in healthcare is not readily found at the clinical bedside. Several
research papers demonstrate that AI performs better than humans at tasks such as diagnosing
disease. For example, deep learning algorithms outperform radiologists at spotting malignant
tumors. Yet these “superior” disease-detecting algorithms largely remain in the labs. Will these
, machine learning diagnostic tools meet the same fate as the expert systems of the 20th century?
Will it be many years before AI substantially augments humans in clinical settings?
This is not the 1970s; AI now permeates healthcare in a variety of ways—in production, for
example. Artificial intelligence helps researchers in drug creation for various diseases, such as
cancer. Beth Israel Deaconess Medical Center, a teaching hospital of Harvard Medical School,
uses AI to diagnose possible terminal blood diseases. It uses AI-enhanced microscopes to scan for
injurious bacteria like E. coli in blood samples, working at a faster rate than manual
scanning. Natural language processing is widely used for automating the extraction and encoding
of clinical data from physician notes. Multiple tools at work in production settings today use
natural language processing for clinical coding. Machine learning helps with steering patients to
the optimal providers. For decades, machine learning has been used to identify fraud and reduce
waste. The widespread adoption of AI in specific use cases in healthcare companies, coupled with
recent innovations in AI, holds tremendous promise for expanding the use of AI in clinical settings.
This book on AI-First healthcare hopes to show a different future for the widespread adoption of
AI in healthcare, including in clinical settings and in people’s homes. There continues to be an
active conversation among clinicians and technologists about implementing AI in healthcare. A
2019 symposium attended by clinicians, policy makers, healthcare professionals, and computer
scientists profiled real-world examples of AI moving from the lab to the clinic.1 The symposium
highlighted three themes for success: life cycle planning, stakeholder involvement, and
contextualizing AI products and tools in existing workflows.
A lot has changed in the years since the conception of neural networks in 1943. AI continues to
evolve every decade, explaining why consensus on an AI definition remains elusive. Since we are
not all operating with the same definition, there is a lot of confusion around what AI is and what
it is not. How artificial intelligence is defined can depend on who provides the explanation, the
context, and the reason for offering a definition. AI is a broad term representing our intent to build
humanlike intelligent entities for selected tasks. The goal is to use fields of science, mathematics,
and technology to mimic or replicate human intelligence with machines, and we call this “AI.” In
this book, we will explore several intelligent entities, such as augmented doctors, prediction
machines, virtual care spaces, and more, that improve healthcare outcomes, patient care,
experiences, and cost.
We continue to build and exhibit systems, machines, and computers that can do what we previously
had understood only humans could do: win at checkers, beat a reigning world chess master, best
the winningest Jeopardy! contestants. Famously, the computer program AlphaGo has defeated
world champions in the 4,000-year-old abstract strategy game Go and excels in emulating (and
surpassing) human performance of this game. Articles abound on machine learning models in labs
outperforming doctors on selected tasks, such as identification of possible cancerous tumors in
imaging studies; this suggests that AI may eventually replace some physician specialties, such as
radiologist.
AI and Machine Learning
