IMIA Yearbook of Medical Informatics 2019
© 2019 IMIA and Georg Thieme Verlag KG
The Price of Artificial Intelligence
Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia
We are not ready for what is about to come.
It is not that healthcare will be soon run
by a web of artiﬁcial intelligences (AIs) that
are smarter than humans. Such general AI
does not appear anywhere near the horizon.
Rather, the narrow AI that we already have,
with all its ﬂaws and limitations, is already
good enough to transform much of what we
do, if applied carefully.
Amara’s Law tells us that we tend to
overestimate the impact of a technology in
the short run, but underestimate its impact
in the long . There is no doubt that AI has
gone through another boom cycle of inﬂated
expectations, and that some will be disap-
pointed that promised breakthroughs have
not materialized. Yet, despite this, the next
decade will see a steadily growing stream
of AI applications across healthcare. Many
of these applications may initially be niche,
but eventually they will become mainstream.
Eventually they will lead to substantial
change in the business of healthcare. In
twenty years time, there is every prospect
the changes we ﬁnd will be transformational.
Such transformation however comes with
a price. For all the beneﬁts that will come
through improved efficiency, safety, and
clinical outcomes, there will be costs . The
nature of change is that it often seems to appear
suddenly. While we are all daily distracted try-
ing to make our unyielding health system bend
to our needs using traditional approaches,
disruptive change surprises because it comes
from places we least expected, and in ways we
never quite imagined.
In linguistics, the Whorf hypothesis says
that we can only imagine what we can speak
of . Our cognition is limited by the concepts
we have words for. It is much the same in the
world of health informatics. We have devel-
oped strict conceptual structures that corral AI
into solving classic pattern recognition tasks
like diagnosis or treatment recommendation.
We think of AI automating image interpreta-
tion, or sifting electronic health record data
for personalized treatment recommendations.
Most don’t often think about AI automating
foundational business processes. Yet AI is
likely to be more disruptive to clinical work
in the short run than it will be to care delivery.
Digital scribes, for example, will steadily
take on more of the clinical documentation task
. Scribes are digital assistants that listen to
clinical talk such as patient consultations. They
may undertake a range of tasks from simple
transcription through to the summarization of
key speech elements into the electronic record,
as well as providing information retrieval and
question-answering services. The promise of
digital scribes is a reduction in human docu-
mentation burden. The price for this help will
be a re-engineering of the clinical encounter.
The technology to recognize and interpret
clinical speech from multiple speakers, and
to transform that speech into accurate clinical
summaries is not yet here. However, if humans
are willing to change how they speak, for
example by giving an AI commands and hints,
then much can be done today. It is easier for
a human to say “Scribe, I’d like to prescribe
some medication” than for the AI to be trained
to accurately recognize whether the speech it
is listening to is past history, present history,
or prescription talk.
The price for using a scribe might also be
an even more obvious intrusion of technol-
ogy between patient and clinician, and new
risks to patient privacy because speech data
contains even more private information than
clinician-generated records. Clinicians might
simply replace today’s effort in creating
records, where they have control over con-
tent, to new work in reviewing and editing
automated records, where content reﬂects
the design of the AI. There are also subtler
risks. Automation bias might mean that many
clinicians cease to worry about what should
go into a clinical document, and simply
accept whatever a machine has generated
Introduction: Whilst general artificial intelligence (AI) is yet to
appear, today’s narrow AI is already good enough to transform
much of healthcare over the next two decades.
Objective: There is much discussion of the potential benefits
of AI in healthcare and this paper reviews the cost that may
need to be paid for these benefits, including changes in the way
healthcare is practiced, patients are engaged, medical records are
created, and work is reimbursed.
Results: Whilst AI will be applied to classic pattern recognition
tasks like diagnosis or treatment recommendation, it is likely to
be as disruptive to clinical work as it is to care delivery. Digital
scribe systems that use AI to automatically create electronic
health records promise great efficiency for clinicians but may lead
to potentially very different types of clinical records and work-
flows. In disciplines like radiology, AI is likely to see image inter-
pretation become an automated process with diminishing human
engagement. Primary care is also being disrupted by AI-enabled
services that automate triage, along with services such as tele-
medical consultations. This altered future may necessarily see an
economic change where clinicians are increasingly reimbursed for
value, and AI is reimbursed at a much lower cost for volume.
Conclusion: AI is likely to be associated with some of the biggest
changes we will see in healthcare in our lifetime. To fully engage
with this change brings promise of the greatest reward. To not
engage is to pay the highest price.
Artificial intelligence, electronic health record, radiology, primary
care, value-based care
Yearb Med Inform 2019:14-5
Published online: 25.04.2019
IMIA Yearbook of Medical Informatics 2019
The Price of Artificial Intelligence
. Given the widespread use of copy and
paste in current day electronic records ,
such an outcome seems a distinct possibility.
At this moment, narrow AI, predomi-
nately in the form of deep learning, is making
great inroads into pattern recognition tasks
such as diagnostic radiological image inter-
pretation . The sheer volume of training
data now available, along with access to
cheap computational resources, has allowed
previously impractical neural network archi-
tectures to come into their own. When a price
for deep learning is discussed, it is often in
terms of the end of clinical professions such
as radiology or dermatology . Human
expertise is to be rendered redundant by
The reality is much more nuanced. Firstly,
there remain great challenges to generalizing
narrow AI methods. A well-trained deep
network typically does better on data sets
that resemble its training population . The
appearance of unexpected new edge cases,
or implicit learning of features such as clin-
ical workﬂow or image quality , can all
degrade performance. One remedy for this
limitation is transfer learning , retraining
an algorithm on new data taken from the
local context in which it will operate. So, just
as we have seen with electronic records, the
prospect of cheap and generalizable technol-
ogy might be a fantasy, and expensive system
localization and optimization may become
the lived AI reality.
Secondly, the radiological community
has reacted early, and proactively, to these
challenges. Rather than resisting change,
there is strong evidence not just that AI is
being actively embraced within the world
of radiology, but also that there is an under-
standing that change brings not just risks, but
opportunities. In the future, radiologists might
be freed from working in darkened reading
rooms, and emerge to become highly visible
participants to clinical care. Indeed, in the
future, the idea of being an expert in just a
single modality such as image interpretation
may seem quaint, as radiologists transform
into diagnostic experts, integrating data from
multiple modalities from the genetic through
to the radiologic.
The highly interconnected nature of
healthcare means that changes in one part
of the system will require different changes
elsewhere. Radiologists in many parts of the
world are paid for each image they read. With
the arrival of cheap bulk AI image interpre-
tation, that payment model must change. The
price of reading must surely drop, and expert
humans must instead be paid for the value
they create, not the volume they process.
The same kind of business pressure is
being felt in other clinical specialties. In
primary care, for example, the arrival of
new, sometimes aggressive, players who base
their business model on AI patient triage and
telemedicine is already problematic [12, 13].
Patients might love the convenience of such
services, especially when they are technolog-
ically literate, young, and in good health, but
they may not always be so well served if they
are older, or have complex comorbidities .
Thus, AI-based primary care services might
end up caring for proﬁtable low-cost and low-
risk patients, and leave the remainder to be
managed by a ﬁnancially diminished existing
primary care system. One remedy to such a
risk is again to move away from reimburse-
ment for volume, to reimbursement for value.
Indeed, value-based healthcare might arrive
not as the product of government policy, but
as a necessary side effect of AI automation.
There are thus early lessons in the different
reactions to AI between primary care and
radiology. One sector is being caught by sur-
prise and playing catch up to new commercial
realities that have come more quickly than
expected; the other has begun to reimagine
itself in anticipation of becoming the ones
that craft the new reality. The price each
sector pays is different. Proactive preparation
requires investment in reshaping workforce,
and actively engaging with industry, con-
sumers, and government. It requires serious
consideration of new safety and ethical risks
. In contrast, reactive resistance takes a toll
on clinical professionals who rightly wish to
defend their patients’ interests, as much as their
own right to have a stake in them. Unexpected
change may end up eroding or even destroying
important parts of the existing health system
before there is a chance to modernize them.
So, the fate of medicine, and indeed for
all of healthcare, is to change . As change
makers go, AI is likely to be among the
biggest we will see in our time. Its tendrils
will touch everything from basic biomedical
discovery science through the way we each
make our daily personal health decisions. For
such change we must expect to pay a price.
What is paid, by whom, and who beneﬁts, all
depend very much on how we engage with
this profound act of reinvention. To fully
engage brings promise of the greatest reward.
To not engage is to pay the highest price.
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