This is Carly Beam with Becker Spine and
Orthopedics podcast. And joining us today is doctor
Morgan L'Oreal, a spine surgeon and past president
of the International Society for the Advancement of
Spine Surgery.
And today, we're gonna have a great chat
about,
AI and spine surgery and where AllSource kinda
fits in the larger
health care equation. So, doctor Lorio, thank you
so much for joining us.
Thank you, Carly. It's a pleasure to be
here. Oh, it's great to have you back
on the podcast. And before we dive into
our questions, could you just give a a
quick overview of your background and your work?
Okay. Well, I've, been a spine surgeon and
a hand surgeon
and,
past president of ISAS. And,
I did a lot of work,
for policy,
and I chaired coding and reimbursement task force
for ISAS as well. So that's pretty much
been my interest, and
AI kind of,
fits in here because of its impact on
policies is is it impacts,
both health care delivery
and,
frankly,
what we write,
and what we do moving forward.
So, hopefully, this is relevant.
Absolutely. And, I mean, obviously, AI is is
is everywhere
in health care and health care conversations.
I'd love to hear start off how you
think about its role when it comes to
spine surgery.
Well, I I use AI, and I value
it. It's an extraordinary
analytical tool.
If you step back for a moment, the
scale of this technology,
becomes clearer.
We remember the Internet took decades to reshape
society,
and today,
we recognize the lack of Internet access represents
a form
of inequality. And so artificial intelligence is arriving
very differently.
Its growth isn't gradual. It's almost vertical. And
now that capability sits inside the devices
we carry in our pockets.
But I think the real risk,
is often misunderstood.
By that, I mean, the real
risk isn't that AI will get things wrong,
that it will succeed
where we
while we slowly
forget how.
In spine care, the the highest value work
often happens
before any incision is made.
That's when a surgeon integrates imaging,
biomechanics,
patient history,
comorbidities,
expectations,
and risk tolerance into a coherent
clinical judgment.
Mhmm. Those cognitive steps are difficult to measure
and rare or rarely reimbursed
in proportion to their importance,
but they determine
whether the right patient
receives the right intervention
at the right time.
It's really fascinating way of looking at it
too, just the idea that AI
is coming in before,
the actual surgery. But, obviously, some people
are worried that AI might replace their jobs
and including,
you know, physicians. Do you see that being
a concern?
Well, I I don't think that replacement's the
real issue,
specifically for surgeons.
Health care
already operates through algorithms,
reimbursement
rules,
prior authorization
systems,
coverage policies,
and outcome reporting structures.
But I can say this, AI will not
enter medicine neutrally.
Artificial intelligence will likely arrive through those same
systems I mentioned.
Unfortunately,
skill erosion
rarely shows up in the metrics we measure.
Accuracy can improve
while human judgment quietly
declines.
So I guess the real question becomes governance.
Yeah. Can you dive into that more governance
specifically?
Yeah.
Complex patients don't always fit neatly
inside efficient systems.
Historically,
the surgeon in the loop judgment
has allowed physicians to recognize
when a patient doesn't fit the algorithm
and to treat the patient
rather than the pathway.
Medicine requires flexibility because
real patients are real complicated.
Systems, however,
often prefer
predictability.
Maintaining physician oversight, therefore,
becomes critical as AI becomes embedded in those
systems.
Mhmm. It's an interesting balance, it sounds like,
between the algorithms and then
figuring out those one off cases.
Can you give an example of where physician
judgment still
matters even when AI is helping out as
a tool?
Sure.
Well, I I recently explored a cancer some
cancer trend data,
within an AI system.
And when I asked whether cancer incidence had
changed after COVID,
the analysis suggested
that incident rates were largely unchanged.
Mhmm. But when I examined the time series
more closely,
something about the pattern,
didn't quite fit in the data points.
The COVID years were clearly disruptive,
so we treated that period as difficult to
interpret
and step back to examine broader trends.
When we then asked
a different question focusing on cancer mortality
instead of incidence,
the pattern appeared different. Mhmm. Mortality
seemed meaningfully higher than earlier baseline patterns
roughly
on the order of about sixteen percent.
That is a substantial
change.
The the AI system
hadn't really made a mistake per se. It
had answered the question it was asked,
but but it reminded me of something important.
AI answers questions
very well.
Sometimes it narrows
answers
simultaneously.
Physicians
still have to decide
which questions matter.
Sometimes the insight comes from recognizing that the
question itself
needs to change.
You're a fascinating idea there, and we'll circle
back to that in a moment. I also
wanna pick your brain on health systems and
how many of them often they hope that
AI will make care more efficient.
What are your thoughts on that?
Well, efficiency is important, but efficiency can also
introduce blind spots.
Sometimes I think about this using a GPS
analogy.
Mhmm. You know, navigation systems
optimize
routes based on filters,
minimizing time, avoiding tolls, reducing distance.
But, occasionally, those filters lead you into loops
or take you off the main road only
to bring you back again later.
The system is optimized
or optimizing
based on the parameters
it was given.
AI functions in a similar way.
AI can optimize the route,
but someone still has to decide
where we're going. And as we move forward,
these filters, they're gonna are gonna become more
and more important.
Yeah. And, doctor L'Oreal, can you talk about
the role you physicians should play
as AI is becoming more integrated into the
work they do?
Yeah.
So physicians need to remain actively involved in
the governance of these systems,
not just as users,
but as stewards.
Complex patients often generate more uncertainty
and variability in outcomes,
but those are frequently the patients who need
the most thoughtful care.
If systems begin to prioritize
predictability
above all else,
there's a risk
that the most complex patients
may become harder to treat.
Just
over a year ago, I had the opportunity
to participate
in a global
fifty roundtable organized by the Dubai Future Foundation,
where experts from multiple sectors were asked to
consider possible futures
roughly five decades from now.
My perspective in that discussion came from two
places.
My international policy work in spine surgery through
ISAS,
and my experience as a practicing surgeon,
one idea
emerging from that initiative is a classification framework
describing how humans
and artificial intelligence collaborate in the production of
knowledge and decisions. Mhmm.
The the framework is a simple visual markers
to indicate the degree of human involvement
in AI assisted work ranging from fully human
activity to machine dominant processes
with several intermediate stages representing different levels of
collaboration and oversight.
The purpose
is transparency.
As artificial intelligence becomes more deeply embedded in
research, analysis, and communication,
it will become increasingly difficult to determine how
much of an output reflects human judgment versus
machine computation.
Mhmm. A clear labeling system helps make that
relationship
visible.
From a clinical perspective,
this aligns closely with something we have long
emphasized in medicine,
surgeon in the loop governance.
Artificial intelligence may assist with analysis,
pattern recognition,
and data synthesis, but clinical responsibility
and decision authority
remain human.
In the terminology of the Dubai framework,
this approach would be classified as human led
AI.
A fascinating way to describe it, human led
AI.
And can you talk about
how AI
might influence the way medical knowledge itself is
developing?
Yeah. There's another dimension to this conversation that
we don't always acknowledge.
AI
increasingly influences how we write,
whether drafting clinical notes, summarizing literature,
or producing scientific manuscripts.
When technology
influences how we write,
it inevitably
influences what we write.
Knowing who
or what
produced it is therefore important.
This can be categorized across six levels,
human only,
human directed,
human led,
machine assisted,
machine dominant,
and machine only.
In fact, journals are already starting to formalize
this principle.
At the International Journal of Spine Surgery,
new author guidelines make it clear that AI
can assist
with drafting or organization,
but scientific
judgment
and clinical interpretation
must remain human led.
And over that time,
that shapes will be collectively known,
What we believe
and ultimately how we practice medicine.
AI systems apply filters,
optimization
rules,
and administrative constraints.
Capability
will proceed
consent,
and adoption
will proceed reflection.
Most importantly,
those filters can gradually direct the flow of
conversation, emphasizing some questions
while quietly discouraging others. I can't emphasize how
important and dangerous these filters are and may
become.
When systems rewrite rather than carefully amend existing
work, portions of the original meaning can quietly
disappear.
The danger isn't censorship or deception.
The danger is continuous optimization where erosion of
skills slowly becomes framed as efficiency.
And that matters in medicine because the way
knowledge is framed ultimately shapes
how diseases are defined,
classified,
and treated.
Definitely. And I like what you said about
what you're doing
at ISAS and then you in the journal
and how you're,
kind of
making guidelines based off of what we know
now with AI. Can you dive deeper into
how all this relates specifically to spine care?
Sure. Well, in spine care, this matters because
chronic low back pain, as I mentioned, is
not a single disease, for instance. It includes
multiple phenotypes,
discogenic pain, retibrogenic pain, sacroiliac dysfunction,
multifidus muscle failure,
neuropathic and radicular pain,
and central sensitization patterns.
AI can help us recognize these phenotypes earlier
and more consistently.
But if surrounding systems favor simplified treatment pathways
or if they narrow outcome metrics,
those distinctions can gradually
disappear from the conversation.
So the issue isn't simply whether AI helps
analyze data. It's whether physicians remain actively
engaged
in shaping how knowledge itself is generated and
applied.
Got it. And doctor Lohr, I want to
also touch on this idea of machine memory
versus human meaning. Can you dive into that
and just, you know, the kind of the
tensions that exist in that realm?
Okay. Well,
speaking to that tension as well, artificial,
intelligence systems accumulate information
indefinitely.
They do not forget.
Humans
are very different.
Our memory is finite.
We cannot retain everything, and so we must
choose what is worth remembering.
What matters is remembering the sequence.
What humans did before systems,
when judgment began to be deferred,
and when dependence
quietly
replace skill.
Maintaining skill will be crucial if humans are
to provide resilience
for this new AI ecosystem.
That act
of selection
deciding
which observations
matter enough to carry forward is actually a
fundamental part of how knowledge develops in medicine.
AI may store and synthesize vast amounts of
information,
but physicians still determine which patterns are meaningful
and which insights
deserve to shape future care. AI remembers everything,
but bureaucracies
bureaucracies, they decide what is allowed to matter.
Artificial intelligence
didn't invent these these tendencies.
It simply accelerated them.
That's very well said, doctor L'Oreal. And before
we wrap up, if you had to summarize
a key lesson for physicians
about AI, what would it be?
Okay. Well, artificial intelligence is gonna become
extraordinarily good in answering questions.
The future of medicine will depend on whether
physicians continue to ask the right ones.
Technology can analyze the data.
Physicians still will have to recognize,
the meaning.
The real risk isn't that AI
will replace
physicians.
The real risk
is that it will slowly replace
the habits of thinking
that make physicians
necessary.
And in that vacuum,
no one will be left to recognize or
remember the meaning.
Very well said. Well, doctor Lorio, thank you
again for joining us in the podcast today.
It's a great conversation, and I hope to
connect again in the future. Thank you, Carly.
I appreciate this conversation and time to be
with you.