TRANSCRIPT
Welcome And What’s Ahead
Deborah Borfitz
Hello
and
welcome
to
the
Scope
of
Things
podcast,
a
no-nonsense
look
at
the
promise
and
problems
of
clinical
research,
based
on
a
sweep
of
the
latest
news
and
emerging
trends
in
the
field,
and
what
I
think
is
worthy
of
your
30
or
so
minutes
of
time.
I'm
Deborah
Borfitz,
Senior
Science
Writer
for
Clinical
Research
News,
which
means
I
spend
a
lot
of
time
with
my
ear
to
the
ground
on
your
behalf,
and
a
lot
of
hours
every
week
speaking
to
top
experts
from
around
the
world.
Please
consider
making
this
your
trusted
go-to
channel
for
staying
current
on
things
that
matter,
whether
they
give
us
hope
or
cause
for
pause.
In
just
a
few
minutes,
I'll
be
speaking
with
Raj
Indupuri,
CEO
of
eClinical
Solutions,
about
lessons
learned
in
scaling
AI
in
real-world
trials
for
some
big
name
pharma
Rapid-Fire Clinical Research Headlines
Deborah Borfitz
companies.
But
first,
the
latest
news,
including
a
new,
applause-winning
treatment
for
pancreatic
cancer,
a
trial
emulation
method
for
improving
rural
health
research,
filling
evidence
gaps
in
drug
safety
during
pregnancy,
guidelines
for
using
routinely
collected
health
data
for
research
purposes,
a
new
proposal
from
the
UK's
Medicines
and
Healthcare
Regulatory
Agency,
and
an
innovative
participant
engagement
portal
enabling
bidirectional
communication
with
patients.
Breakthrough KRAS Drug For Pancreatic Cancer
Deborah Borfitz
Revolution
Medicine's
new
oral
KRAS
inhibitor
therapy
for
pancreatic
cancer
drew
loud
audience
applause
and
a
standing
ovation
at
the
recent
American
Society
of
Clinical
Oncology
annual
meeting
when
phase
three
clinical
trial
results
were
announced,
bringing
fresh
hope
to
patients
and
oncologists.
The
paradigm-shifting
investigational
drug,
which
the
FDA
has
already
granted
expanded
access,
almost
doubled
life
expectancy
in
the
metastatic
patients
who
took
it
compared
to
similar
patients
who
had
chemotherapy.
Full
approval
by
the
agency
is
expected.
The
agent
targets
the
undruggable
KRAS
gene
that's
mutated
in
over
90%
of
pancreatic
cancers,
which
are
generally
diagnosed
at
an
advanced
stage.
Target Trial Emulation For Rural Research
Deborah Borfitz
Researchers
in
Australia
have
come
up
with
a
new
method
to
improve
health
research
in
rural
areas
when
randomized
controlled
trials
aren't
possible
due
to
small
populations,
limited
infrastructure,
and
workforce
constraints.
Their
answer
is
target
trial
emulation
using
data
that
has
already
been
collected
during
routine
care
to
replicate
the
design
of
an
ideal
RCT
using
real-world
data
and
thereby
supporting
the
development
of
learning
health
systems.
The
approach
is
especially
well
suited
to
enabling
telehealth
workforce
initiatives
and
point-of-care
diagnostics
to
be
researched
quickly
and
rigorously.
AI To Close Pregnancy Drug Safety Gaps
Deborah Borfitz
A
new
report
from
the
Journal
of
Medical
Internet
Research
finds
that
AI
can
fill
evidence
gaps
in
drug
safety
during
pregnancy
based
on
two
novel
efforts:
the
Boost
HP
project
using
a
tree-based
approach
to
data
mining,
allowing
decision
pathways
to
be
transparently
traced,
and
the
bionic
study
that
combines
causal
inference
and
machine
learning.
The
research
approach
lets
AI
do
the
heavy
lifting
of
analyzing
large
data
sets
and
by
identifying
safety
signals
and
high-risk
subgroups
could
enable
more
targeted
and
ethically
designed
trials
that
include
pregnant
participants
earlier
in
the
evidence
generation
process.
Rules For Using Routine Health Data
Deborah Borfitz
An
international
research
consortium
has
published
first-of-its-kind
guidelines
on
the
use
of
routinely
collected
health
data
for
research
purposes
to
improve
the
quality,
validity,
and
transparency
of
studies
based
on
data
such
as
that
from
electronic
health
records
and
registries.
Emphasis
was
placed
on
the
risk
of
biased
results
as
well
as
on
the
problems
associated
with
missing
and
erroneous
data,
and
the
role
of
AI-based
analytical
methods.
The
study
includes
recommended
actions
researchers
can
take
that
would
help
prevent
misinterpretations,
increase
the
reproducibility
of
studies,
and
strengthen
trust
in
results
derived
from
routine data.
UK Rare Disease Regulatory Rethink
Deborah Borfitz
The
UK's
Medicines
and
Healthcare
Regulatory
Agency
has
proposed
a
rare
disease
therapies
framework
that
introduces
significant
regulatory
innovation
to
the
country's
rare
disease
landscape.
At
the
heart
of
the
proposal
is
a
single
investigational
marketing
authorization
designation
that
combines
clinical
trial
approval
with
a
progressive
route
to
marketing
authorization
that
includes
rolling
data
submissions,
modular
assessments,
and
earlier
patient
access.
The
guidance
supports
adaptive
and
innovative
trial
designs,
accepts
that
surrogate
or
patient
relevant
endpoints
may
sometimes
be
appropriate,
and
acknowledges
the
potential
scientific
value
of
computational
modeling,
digital
twins,
and
non-animal
methods.
The
agency
has
urged
the
pharmaceutical
and
life
sciences
industries
to
weigh
in
on
the
proposals
by
the
end
of
July.
Patient Portal Built For Retention
Deborah Borfitz
And
finally,
a
new
participant
engagement
portal
developed
by
the
Alliance
for
Clinical
Trials
and
Oncology
completely
flipped
the
traditional
digital
health
tool
framework
by
focusing
on
retention
and
bi-directional
communication
with
patients
who
are
already
enrolled
rather
than
recruitment
and
onboarding.
Among
the
900
participants
in
the
NCI-sponsored
multi-cancer
early
detection
biobank
study
who
opted
to
use
the
portal,
84%
reported
having
a
positive
experience,
and
93%
agreed
to
be
contacted
for
future
research
opportunities.
The
portal,
which
has
password-free
access
as
well
as
accessible
and
multilingual
design,
was
most
successful
at
local
community
clinics,
outperforming
larger
academic
medical
centers
in
patient
enrollment
rate
and
participation.
The
tool
now
will
be
expanding
across
more
national
clinical
trials.
As
a
reminder,
links
to
the
articles,
studies,
and
press
releases
referenced
in
this
month's
news
segment
can
be
found
in
the
show
notes.
Three Real Barriers To AI Adoption
Deborah Borfitz
I
am
now
delighted
to
bring
to
the
mic
eClinical
Solutions
Raj
Indupuri
for
some
practical
real-world
insights
on
overcoming
adoption
barriers
with
AI
in
modern-day
clinical
trials.
Welcome
to
the
show,
Raj.
Raj Indupuri
Thank
you,
Deborah,
for
having
me.
Quite
exciting.
Deborah Borfitz
Yes,
yes.
Thank
you
so
much
for
joining
us.
As
many
of
our
listeners
likely
already
know,
there
has
been
a
huge
upsurge
in
the
adoption
of
AI
agents
by
clinical
trial
support
companies
over
the
last,
I
don't
know,
months
or
years.
But
from
my
read,
the
embrace
of
these
tools
by
sponsors
and
CROs
has
been
more
measured
than
frantic.
That's
probably
a
good
thing.
But
it
also
suggests
a
hesitancy
that
I'm
sure
has
a
lot
of
sources.
Raj,
what
in
your
conversations
with
industry
players
would
you
cite
as
the
top
three
holdups
in
terms
of
simply
considering
deployment
of
an
AI
agent
for
any
reason
or
perhaps
some
task-specific
reasons?
Raj Indupuri
Yeah,
the
top
three
barriers
from
our
perspective
are
data
readiness,
governance
and
trust,
and
operating
model
change.
And
to
be
blank,
if
your
data
foundation
is
fragmented,
an
AI
agent
does
not
solve
the
problem.
It
just
scares
the
confusion.
And
as
you
are
aware,
our
industry
is
quite
fragmented
where
you
have
different
sponsors
working
in
a
different
way.
Every
CR
is
different,
every
trial
is
different.
There's
so
much
data
that
we
have
been
collecting,
right?
The
modalities
and
how
you
bring
all
this
data
is
different.
So
it's
quite
fragmented.
And
if
you
don't
have
strong
modern
data
foundations,
AI
agents
would
not
be
able
to
work
effectively.
And
that's
the
biggest
barrier
from
our
perspective.
And
when
it
comes
to
the
second
one
that
I
referred
to,
governance
and
trust
in
real-grid
environment
environments.
Teams
need
to
know
exactly
how
an
agent
arrived
at
a
recommendation.
They
need
lineage,
explainability,
audit
rails,
and
it
cannot
be
a
black
box
because
at
the
end,
you
are
going
to
influence
safety
and
submission
quality
with
these
agents.
So
that's
another
big
barrier.
And
the
third
one
obviously
is
the
operating
model
that
I
was
referring
to.
And
this
requires
a
workflow
redesign.
And
also
I
keep
referring
to
internally
with
our
teams
in
terms
of
how
we
need
to
change
the
way
we
work
with
these
agents.
And
if
I
can
elaborate
on
this,
if
you
take
data
review,
which
is
a
big
manual
task
with
clinical
trials,
the
real
shift
is
from
reviewing
everything
to
focusing
more
on
exceptions
or
signals,
right?
This
requires
an
organizational
or
an
operating
model
redesign
and
a
workflow
redesign.
So
we
believe
the
organization
is
making
the
fastest
progress
are
the
ones
that
understand
the
execution
layer
is
the
real
work,
not
the
underlying
model,
not
the
point
solution
sitting
on
top
of
these
fragmented
systems.
Deborah Borfitz
Nonetheless,
Where AI Agents Get Early Traction
Deborah Borfitz
we
do
have
a
lot
of
agents
out
there.
There's
a
whole
bunch
of
them.
And
and
probably
we're
going
to
get
back
to
someone
you're
just
talking
about
when
we
start
speaking
about
the
specific
agents
that
eClinical
solutions
has
as
Illuminate
agents,
I
think
is
how
you
pronounce
it.
I
think
there's
like
four
of
them
now.
Data
review,
of
course,
you
just
talked
about
data
review,
but
also
data
mapping
and
risk-based
quality
management
and
study
operations,
all
I
guess
part
of
your
clinical
data
cloud.
And
I
think
only
available
starting
this
year,
if
I'm
not
mistaken.
So
I
know
these
are
early
days,
but
but
what
has
been
the
sort
of
pattern
of
uptake
with
these
agents
so
far?
And
what
specific
ways
are
they
initially
to
be
used?
Or
are
there
areas
where
companies
are
particularly
reluctant
to
give
AI
a
role
at
all?
Raj Indupuri
Yeah,
great
question.
So
the
clearest
uptake
from
our
site,
what
we
have
seen,
is
where
the
pain
is
already
measurable.
And
with
our
Illuminate
Clinical
Data
Intelligence
platform,
we
provide
different
capabilities
across
the
clinical
development
value
chain
and
primarily
focused
on
data
review,
data
mapping
insights,
risk-based
quality
workflows.
So,
as
you
mentioned,
so
we
we
have
released
agents
in
Q4,
or
we
started
working
and
then
we
brought
them
to
production
in
this
year.
And
still
early
days,
but
we
have
seen
significant
interest
and
clear
uptake
in
terms
of
both
data
mapping
and
data
review.
So,
prior
to
this
agentic
workflows,
so
the
amount
of
time
it
takes
to
map
all
this
raw
data
to
standardized
data
or
some
kind
of
a
model
for
insight,
it's
quite
cumbersome
and
time
consuming.
And
our
customers
see
tremendous
value
in
automating
data
review
using
this
data
review
agent.
That's
that's
an
area
where
we
are
seeing
good
success,
and
we'll
continue
to
invest
heavily
into
that.
And
the
other
big
one
is
the
example
I
gave
earlier
is
data
review.
The
amount
of
data
that
we're
collecting
is
only
increasing,
and
it
puts
an
enormous
burden
on
reviewers,
medical
monitors,
clinical
scientists
to
do
all
this
review
and
also
interpret
this
data
and
act
on
this
data.
So
the
agent
or
agents
that
we're
working
on,
we
have
seen
incredible
success
where
users
can
use
different
personas
or
different
stakeholders
can
use
this
data
review
agent
or
agents
and
quickly
get
insights
and
act
on
this
data
for
faster
decision
making.
So
we
have
quite
excited
in
terms
of
adoption
with
these
agents.
So
our
big
differentiator
with
us
is
our
agents
they
work
together
on
the
same
governed
data
foundation.
They're
not
isolated
bots,
they
share
the
context,
data
lineage,
and
also
the
workflow
logic
so
that
the
insight
from
one
area
can
immediately
inform
action
in
another.
So
this
is
where
I
think
we
believe
we'll
differentiate
from
others.
And
also,
this
is
where
the
difference
would
show
up
between
experimentation
and
production,
right?
A
useful
agent
does
not
generate
an
answer,
it
helps
produce
an
answer
that
a
clinical
team
can
trust,
explain,
defend,
and
act
on.
Deborah Borfitz
Okay,
very
good.
Good
explanation.
I
appreciate
that.
Announcement
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Glass Box Governance And Trust
Announcement
review.
Deborah Borfitz
And
I
want
to
get
back
to
something
you
sort
of
talked
about
from
the
get-go
here.
And
this
was,
I
was
going
to
talk
about
my
experience
at
the
recent
SCOPEX
conference,
where
some
of
the
most
covered
topics
were
the
very
things
we've
been
talking
about:
governance,
trust,
regulatory
readiness,
sort
of
things,
and
the
importance,
especially,
of
humans
remaining
central
to
oversight
and
final
judgment.
You
know,
that
was
like
a
big
recurring
theme.
Presumably,
this
reflects
the
prevailing
concerns
of
pharma
companies.
So,
what
would
you
bring
to
the
table
on
these
sort
of
trending
talking
points
beyond
what
you've
shared
already,
particularly
the
human
and
the
loop
piece?
Raj Indupuri
Yeah.
This
is
the
conversation
that
actually
determines
who
wins.
And
this
is
where
I
believe
you're
alluding
to
govern
governance
and
trust
cannot
be
just
check
boxes.
They're
the
foundation
for
the
whether
AI
ever
moves
beyond
pilots
in
a
regulated
and
regulated
environment
like
ours.
So
we
believe
in
a
glass
box
governance
for
every
agent
and
for
every
AI
capability
that
we
build.
By
glass
box,
I
mean
every
recommendation
has
to
be
traceable.
Need
to
provide
visibility
into
what
data
the
agent
used,
what
rule
or
reasoning
path
is
followed,
and
then
where
a
human
made
the
final
decision
that
needs
to
be
logged
and
audited
as
well.
Another
thing
I
should
mention
when
it
comes
to
our
company
and
our
eliminating
later
intelligence
platforms.
Again,
we
have
a
map.
We've
been
working
on
this
for
more
than
a
decade
in
terms
of
our
data
pipeline.
And
it's
this
common
data
that
we
have
access
to.
But
also,
we
have
a
lot
of
experience
with
our
biometric
services.
So
that
execution
expertise
really
helps
build
agents
that
are
more
trustworthy
and
also
more
regulatory
ready.
Raj Indupuri
So
that's
another
an
important
aspect
when
we
are
building
agents
that
need
to
work
in
production.
That
service
execution
knowledge
needs
to
be
encoded
into
the
agents,
and
that's
what
we
are
able
to
do
with
our
experience.
And
also
when
it
when
when
it
when
we
talk
about
a
glass
box,
we
are
also
striving
to
provide
our
customers
visibility
into
the
context
that
has
been
layered,
and
also
we
are
enabling
to
them
to
layer
their
own
context.
Because,
again,
like
I
mentioned
earlier,
every
sponsor
is
different,
right?
So
they
have
their
own
layer
of
context
that's
that's
critical
for
agents
to
perform
really
well
in
their
environment.
And
also,
the
other
thing
is
all
around
evaluations.
You'll
hear
this
word
e-valves
with
agents.
Again,
before
you
actually
use
them
in
production
at
scale,
you
need
to
ensure
that
these
agents
work
really
well.
And
that's
done
through
a
process
called
evaluation
or
e-valves.
And
we
are
providing
our
sponsors
or
customers
with
an
ability
so
that
they
can
do
e-valves
on
these
agents
in
a
self-service
way.
So
that's
the
reason
we
refer
to
this
as
class
box
AI.
So
then
when
agents
are
embedded
into
validated
workflows,
like
what
we
have
with
lineage
controls
and
explainable
outputs,
they
can
reduce
review
restrain
instead
of
creating
more
work.
I
thought
that
foundation
AI
would
become
another
reconciliation
problem
and
a
burden
for
customers,
which
we
are
which
we
are
avoiding.
Human Oversight In Validated Workflows
Deborah Borfitz
Okay,
I
want
to
get
back
to
the
human
oversight
here,
just
sort
of
dig
in
a
bit
more
there.
And
like,
where
would
you
say
that
these
agents
are
most
needing
human
oversight,
you
know,
to
arrive
at
something,
yeah,
a
document
that's
accurate
and
complete?
And
has
this
been
witnessed
in
in
real-world
trials
that
you're
basing
it
on
on
speculation
or
something
that
is
actually
seen
and
witnessed
and
know
is
is
the
case
and
a
problem
or
a
concern?
Raj Indupuri
Yeah.
So
the
agents
that
we
are
deploying
are
not
autonomous
agents.
Because
again,
we
actually
have
work
in
a
regular
environment.
We
are
dealing
with
patient
data,
right?
It's
very
important
that
there
is
this
human
in
the
loop.
So
our
objective
is
to
have
these
agents
invalidated
workflows
and
automate,
help
automate
as
much
as
possible.
But
there's
always
human
in
the
loop
for
actual
judgment,
right?
Because
the
context,
right,
for
a
trial
across
data
sources
is
it
it
there
are
nuances,
right?
So
we
want
to
make
sure
that
the
final
accountability
for
safety
and
integrity
or
regulatory
submission
quality
is
still
trusted
with
humans.
So
the
goal
here
is
to
not
to
remove
humans
from
clinical
trials,
right?
The
goal
is
to
stop
wasting
expertise
or
expert
human
work,
right?
That
machines
can
do
better.
And
especially
with
crypto
tasks
and
a
lot
of
workflows
that
we
have
already
seen
that
could
be
automated.
So
AI
should
handle
this
volume
of
data
review,
and
in
this
case,
again,
going
back
to
review
pattern
recognition
or
any
signal
detection,
and
humans
should
own
judgment,
interpretation,
and
final
accountability.
This
is
the
this
was
the
operating
model
shift
I
was
referring
to
earlier.
Deborah Borfitz
Okay,
very
good.
Yeah.
All
right.
Obviously,
Time Savings From Mapping And Review
Deborah Borfitz
AI
agents
have
a
vast
potential
in
terms
of
improving
clinical
trial
velocity.
You
know,
what
sort
of
time
savings
can
we
reliably
attach
to
different
tests
done
in
conjunction
with
AI
agents?
Is
there
any
case
studies
you
could
point
to
or
um
just
some
anecdotal
anything?
Raj Indupuri
Absolutely.
So
I'd
be
careful
in
terms
of
making
blanket
statements
or
saving
claims
because
again,
the
baseline
varies
enormously
by
each
sponsor
or
each
customer
and
the
study
design
and
complexity
and
also
the
maturity
of
that
particular
data
infrastructure.
But
it
has
been
really
consistent
with
at
least
the
agents
that
we
already
have
deployed
to
production.
Again,
one,
the
mapping
automation
agents
and
the
data
review.
So,
for
example,
with
the
mapping,
it
it
takes
usually
weeks
to
several
weeks,
it
could
be
eight
to
twelve
weeks
to
do
the
mapping
of
this
raw
data
to
standardized
data
for
either
submission
purposes
or
for
advanced
downstream
analytics.
And
we
have
seen
that
with
our
genetic
approaches
and
this
mapping
automation
agent,
we're
bringing
down
it
to
two
to
three
weeks,
right?
So
that's
substantial
reduction.
And
as
we
continue
to
innovate,
we
believe
this
will
be
down
to
literally
days,
right?
So
now
you're
trying
from
something
from
eight
to
twelve
weeks
down
to
literally
like
a
matter
of
days.
That's
a
huge
benefit.
And
especially
when
trials
continue
to
change,
right?
So
we
are
in
a
we
are
in
an
environment
where
there
are
so
many
protocol
amendments
that
happen
for
different
reasons,
right?
It's
not
all
bad.
It
could
be
also
for
doing
trials
in
by
learning
in
real
time
and
making
amendments.
Raj Indupuri
So
you
continuously
change
the
data
structures
that
requires
this
continuous
mapping.
So
over
a
period
of
time
during
the
entire
conduct,
that
leads
to
significant
efficiencies.
The
second
or
the
two
other
cases
are
data
review
again.
The
initial
feedback
I
get
from
data
managers
who
are
using
this
is
what
used
to
take
hours
or
weeks,
right?
Or
I
would
say
days
when
you're
looking
at
certain
data
sets
to
interpret.
Now
it
happens
in
minutes,
literally,
right?
So
if
you're
if
you're
looking
at
a
visualization
or
set
of
charts
or
long
data
sets
or
large
data
sets,
right,
which
have
thousands
and
thousands
of
records,
using
these
agents,
you
can
actually
do
analysis
and
focus
on
actions
in
minutes,
literally
minutes.
So
this
would
all
add
up
when
it
comes
to
conducting
a
trial
with
these
agents.
And
over
a
period
of
time,
this
could
turn
into
significant
efficiencies
and
substantial
compression
of
cycle
times,
and
not
to
mention
the
quality
improvements,
because
majority
of
the
work
is
done
by
these
agents,
right?
And
the
humans
would
be
involved
in
final
judgment,
escalation,
and
accountability.
So
again,
still
early
days,
but
but
we
are
seeing
significant
results.
I
would
I
would
expect
in
the
next
few
months,
right?
This
efficiencies
or
these
savings
can
be
more
quantified
as
we
continue
to
accelerate
and
innovate.
Next Wave Agents And Protocol Intelligence
Deborah Borfitz
Yep,
and
I
will
be
looking
for
that.
You
know,
and
I'm
guessing
also
that
eClinical
solutions
plans
to
build
a
larger
team
of
AI
agents
beyond
the
four
current
ones.
And
if
that's
a
correct
assumption,
where
can
we
expect
to
see
some
more
of
these,
I
don't
know,
new
hires,
if
you
want
to
call
them
that
in
the
months
ahead?
Raj Indupuri
Yeah,
that
that's
a
great
way
to
think
about
it,
right?
So
the
idea
here
is
these
agents
become
your
thought
partners
or
team
members,
like
you're
referring
to
new
hires,
which
is
really
cool.
Yes,
absolutely.
So
we
are
all
in
into
AI
and
agents,
and
the
unit
agents
that
we
assign
what
that
we
announced
are
across
four
product
pillars.
We
have
four
product
pillars.
One
is
data
pipeline,
which
is
a
data
mapping
one
I'm
talking
to,
data
management
analytics,
the
data
review
one.
The
other
one
is
quality
and
risk
management,
another
around
an
agent
to
support
risk
assessments
and
then
operations.
So
the
strategy
and
and
the
roadmap
that
we
have
is
to
build
agents
across
these
pillars.
But
what's
exciting
is
as
we're
working
on
agents,
we
realize
that
there
is
a
need
of
a
fifth
pillar,
product
pillar,
and
which
we
are
referring
to
as
protocol
intelligence.
This
is
something
that
we
are
going
to
announce
very
soon,
but
we
have
made
a
ton
of
progress.
The
idea
here
is
when
you
are
when
you're
executing
these
agents
or
when
when
you're
building
these
agents,
the
agents
require
significant
knowledge
and
also
they
need
phonetics.
And
as
you're
familiar
with
clinical
trials,
the
protocols
have
a
ton
of
knowledge,
right?
So
now
what
we've
decided
to
do
is
build
protocol
intelligence
as
another
pillar,
and
you're
building
agents
so
that
you
can
extract
intelligence
from
the
protocol
and
then
combine
with
all
other
agents,
and
this
becomes
this
control
layer,
and
it
will
connect
all
these
pillars
so
that
at
the
end
of
the
day,
they'll
all
work
together,
and
the
outcomes
that
you
are
going
to
achieve
will
be
quite
substantial
than
looking
at
this
in
a
fragmented
way,
right?
So
the
upside
for
PP
is
not
just
productivity,
it's
going
to
elevate
every
role
in
clinical
development,
data
managers,
reviewers,
statisticians,
biometric
leaders.
It
should
be
spending
less
time
chasing
discrepancies
or
collaborating
with
different
teams
and
more
time
interpreting
on
risk
and
quality
and
patient
impact.
So
we're
quite
excited
in
terms
of
where
we
are
headed.
And
again,
we
already
have
this
competitive
edge
where
we
have
this
governed
data
and
the
validated
workflow.
And
also
because
of
service
experience,
we
have
this
significant
domain
context
knowledge.
And
we
believe
this
approach
will
really
help
deliver
significant
outcomes
for
our
customers.
And
this
is
where,
again,
pilots
will
move
into
production
and
the
agents
will
be
adopted
and
implemented
at
scale
in
the
near
future.
Closing Thanks And Scope Europe Invite
Deborah Borfitz
Yeah,
sure.
We'll
be
seeing
similar
stories
across
the
industry.
Thank
you,
Raj,
for
casting
this
vision
of
AI
agents
as
sort
of
approachable
digital
teammates
rather
than
threatening
competitors
who
may
just
need
a
bit
of
managing
and
mentoring
to
get
up
to
speed
on
their
assigned
tasks.
It's
a
new
day
that
soon
will
stop
feeling
like
it
and
be
the
baseline
operating
standard,
right?
Good
luck
to
you,
any
clinical
solutions
on
the
journey
into
this
still
largely
uncharted
territory
of
human-agent
collaboration.
Raj Indupuri
Thank
you,
Deborah.
I
really
enjoyed
this
conversation.
I
really
loved
the
way
you
were
phrasing
agents
as
teammates
or
new
hires.
I
agree.
I
think
this
is
one
of
the
most
important
shifts
that
are
happening
in
clinical
development.
And
I
appreciate
this
chance
to
discuss
where
the
barriers
are
unlocking.
Deborah Borfitz
So,
I
appreciate
you
taking
the
time.
And
as
always,
a
big
thank
you
to
everyone
out
there
for
listening
in.
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One
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