The
medical
community’s
comfort
with
deploying
AI
in
clinical
care
is
rapidly
evolving
—
because
it
has
to,
according
to
health
informatics
leaders
at
NYU
Langone
Health.
They
said
that
AI
agents
will
likely
be
performing
clinical
tasks
completely
on
their
own
—
with
no
human
in
the
loop
—
in
the
near
future.
Take
blood
pressure
medication
titration
for
example.
“We
already
have
an
AI
assistant
we
built
for
our
home
blood
pressure
monitoring
program
—
that
right
now
still
has
a
human
in
loop
for
doing
the
titrations
of
the
meds.
Five
years
from
now,
we’re
not
going
to
have
a
human
doing
those
titrations,”
said
Dr.
Devin
Mann,
senior
director
for
informatics
innovation
at
NYU’s
Center
for
Healthcare
Innovation
and
Delivery
Science.
Dr.
Paul
Testa,
NYU’s
chief
health
informatics
officer,
agreed,
saying
“there’s
no
reason
to.”
In
his
eyes,
hypertension
management
is
a
clear
example
of
where
full
automation
makes
sense.
Under
current
care
models,
getting
a
patient
to
their
target
blood
pressure
can
take
six
to
nine
months,
largely
because
of
slow,
incremental
medication
adjustments
that
require
repeated
interactions
with
the
health
system
and
its
human
clinicians.
But
those
steps,
Dr.
Testa
said,
follow
well-established
clinical
guidelines
and
rely
on
objective
home
blood
pressure
data
—
making
them
well
suited
for
AI-powered
decision
making.
Full
automation
could
also
significantly
improve
a
patient’s
“time
to
therapy,”
Dr.
Testa
added.
Patients
typically
experience
a
delay
between
diagnosis
and
effective
treatment,
and
this
period
is
often
unnecessarily
long
—
not
because
clinicians
don’t
know
what
to
do,
but
because
the
healthcare
system
moves
slowly,
he
explained.
AI
could
shrink
that
window
by
automating
routine
steps
like
data
review,
guideline-based
decisions
and
patient
follow-ups
to
reach
the
right
treatment
faster,
Dr.
Testa
stated.
He
also
pointed
out
that
there
are
some
clinical
workflows
that
no
longer
require
human
interpretation,
such
as
diabetic
retinopathy
screening.
The
rate
of
screening
for
this
disease
remains
low
nationwide,
hovering
around
15%
—
but
with
full
automation,
Dr.
Testa
argued
that
those
rates
could
approach
100%.
Screening
rates
remain
low
because
the
process
still
depends
on
a
series
of
manual
steps
—
ordering
the
test,
interpreting
results
and
placing
referrals
—
each
of
which
introduces
friction
and
opportunities
for
delay.
Fully
automated
screening
and
referral
could
eliminate
those
handoffs
and
ensure
eligible
patients
are
identified
and
routed
to
care
consistently.
Dr.
Mann
emphasized
that
this
push
for
full
automation
isn’t
just
about
efficiency
or
speed
—
it’s
about
the
fact
that
the
workforce
to
deliver
guideline-recommended
care
simply
doesn’t
exist.
Clinical
guidelines
often
call
for
far
more
lifestyle
counseling
and
ongoing
support
than
health
systems
can
realistically
provide,
he
noted.
In
areas
like
nutrition
and
chronic
disease
management,
the
number
of
clinicians
required
would
be
orders
of
magnitude
higher
than
the
workforce
that’s
actually
out
there.
“There’s
a
missing
workforce
that
[AI]
will
just
step
into.
We’re
never
going
to
hire
50,000
dietitians.
They
don’t
even
exist,
let
alone
the
fact
that
the
reimbursement
is
not
really
there
for
them.
So
[AI]
will,
I
think,
create
roles
that
we
always
wanted
to
be
in
there
with
humans,
but
the
humans
just
aren’t
there,”
Dr.
Mann
said.
He
also
pointed
out
that
human
effort
should
shift
to
relationship-based
and
complex
care.
As
routine
work
is
automated,
clinicians
could
spend
more
time
on
patient
education,
shared
decision
making
and
edge
cases
—
areas
where
persuasion,
trust
and
nuance
still
matter
and
where
AI
struggles.
Taken
together,
Drs.
Mann
and
Testa
see
a
future
in
which
fully
autonomous
AI
is
not
a
fringe
experiment,
but
a
practical
response
to
the
realities
of
modern
healthcare.
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ThongSam,
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