1 .71 billion people. It's quite a number, isn't
it? That's the estimate, worldwide, for people
affected by musculoskeletal conditions. It really
is, and it puts this, well, immense strain on
healthcare systems everywhere, leads to those
long waiting lists we hear about for planned
procedures, and, you know, ultimately hits patient
quality of life. It's a massive challenge for
orthopedics, no question. A huge challenge. Welcome
to the Deep Dive. This is where we take a stack
of research, articles, reports, basically, whatever
sources you bring to us, and we try to distill
the really important knowledge, maybe some surprising
insights, too. Cut through the noise. Exactly.
Get you the essential takeaways. And today we
are doing a deep dive into how digital technology,
and specifically artificial intelligence, are
looking to transform orthopedic care. We're focusing
on planned procedures. Yes, a really fast -moving
area. And guiding us through the material we've
pulled together for this particular dive is our
expert, someone who's brilliant at synthesizing
these sometimes quite complex topics and helping
us understand what really matters. Welcome. Thank
you. It's great to jump into these sources. We've
looked at quite a mix, actually, some detailed
research papers on specific AI uses, broader
reviews on digitalization's impact, and even
some thinking from, well, outside healthcare
on things like data and user engagement. Yeah,
it's a rich set of sources, gives us different
perspectives. So our mission for this deep dive
really understand how AI and these other digital
tools are actually influencing planned orthopedic
care, starting right from assessment and planning
all the way through the surgery itself and into
recovery. The whole pathway. The whole pathway.
So let's kick off with a quick rapid fire round
just to sort of get our bearings. First question
for you. Looking at these sources, what's the
single biggest problem in orthopedics right now
that digital tech is really trying to solve?
Well, the fundamental issue, it seems, that keeps
coming up is just managing the sheer volume of
patients and, consequently, easing the burden
on healthcare systems. Just the numbers. Exactly.
Musculoskeletal conditions are a leading cause
of disability globally. Demands rising, especially
with aging populations. So... digital tools offer
potential ways to handle that scale more efficiently.
Okay, that makes sense. Second question, where
are we seeing the most practical sort of in -use
promise from AI in orthopedic practice today?
What's actually happening now? Right now I'd
say the most concrete applications seem to be
in analyzing imaging, helping process x -rays,
that kind of thing, and definitely assisting
with preoperative planning and also predicting
patient outcomes. Okay. That's where we are seeing
some validated tools starting to emerge. Got
it. And third question, what's the biggest hurdle,
you know, the main sticking point preventing
widespread adoption of these technologies across
orthopedics? Across the board, a really critical
challenge is data integration. Just getting different
systems different types of patient information
to talk to each other effective right the plumbing
pretty much and linked very closely to that is
the need for really robust validation of the
tools themselves building trust both clinician
and patient trust and crucially Navigating the
pretty complex ethical landscape around AI and
health care that frames it perfectly challenge
and opportunity Okay, let's really unpack this.
Let's start at the beginning of that patient
journey Diagnosis and planning. You mentioned
the massive patient volume. Can AI actually help
right at that initial assessment stage? Well,
that's certainly the hope and it's an area where
research is very active. We are seeing applications
of AI machine learning, deep learning being used
to analyze orthopedic images like x -rays. Some
studies mentioned in the sources suggest AI models
can perform well. Comparably to or sometimes
in specific tasks maybe even outperform clinicians
or radiologists really outperform in what sort
of tasks things like detecting certain types
of fractures or identifying and staging how Severe
osteoarthritis is on a standard 2d x -ray deep
learning DL. It's a type of machine learning.
It's particularly good with large amounts of
image data That sounds like it could really speed
things up at the front end. Are there specific
examples in the sources of this kind of AI analysis
being used? Yes, yeah. One source mentions a
deep neural network, a DNN. which is just another
term for a deep learning model that Lim and Colley's
developed specifically for osteoarthritis screening.
Okay. Another study by Ratzlaff used web -based
questionnaires combined with some AI analysis
for initial OA detection. The idea is tools that
could potentially screen patients earlier or
maybe help generate a list of possible diagnoses
to investigate. Okay, but is that... Is that
really ready for prime time, though? Can AI actually
diagnose someone just from, say, an image or
a questionnaire remotely? Well, this is where
the sources give us a bit of a reality check.
Right. Because while these screening tools might
show high sensitivity, they're good at flagging
potential issues, so they don't miss things.
So they catch a lot. They catch a lot, yeah.
But their specificity can be quite low. For instance,
that JamR review highlights a Neoway web questionnaire
with 73 % sensitivity. It sounds good. But it
also notes symptom checker specificity could
be as low as, say, 23 % to 27 % in one study
they looked at. Ah, OK. So high sensitivity,
low specificity means? You get a lot of false
positives. You flag lots of people who don't
actually have the condition. Right. So it might
send perfectly healthy people down the pathway
for more checks. Precisely. And that JMI review
is pretty clear. Despite these developments,
there's actually limited evidence that current
standalone digital health apps can reliably establish
a primary diagnosis remotely, especially when
you compare them to traditional clinical methods.
Like seeing a doctor face -to -face or having
a specialist review detailed scans. Exactly.
And why is that limitation so significant? What
are these digital tools missing compared to,
say, a clinic visit? Well, they often lack the
integration of crucial clinical data. A doctor
gathers information from a physical exam, right?
They interpret imaging, but in the full context
of the patient's history, maybe order lab tests.
The whole picture. The whole picture. Current
remote apps often operate in silos, maybe just
using one or two data streams. Plus, as the Psycho2J
review points out, there's often a mismatch between
how bad arthritis looks on an x -ray and the
patient's actual symptoms. Oh, right. You can
have a bad x -ray but feel OK or vice versa.
Exactly. So an AI relying only on image severity
could easily miss or misinterpret that clinical
nuance. So OK. While AI offers, let's say, tantalizing
potential for initial sifting or providing some
info, it's definitely not replacing the full
diagnostic process yet. It needs more data input,
more context. Precisely. Based on these sources,
the current clinical utility for standalone remote
diagnosis seems quite limited. Okay, that's clear.
Let's move a bit further into those early stages
then, specifically preoperative planning. How's
AI being used there to help surgeons get ready
for a procedure? Now this is an area showing
I think stronger practical application today.
AI is being integrated into software that really
helps with surgical preparation. Things like
helping optimize implant design. There's mention
of a machine learning application for hip stem
design, for instance, or facilitating patient
-specific adjustments, creating really detailed
3D templates based on the patient's imaging data.
So tailoring the plan to the individual. Exactly.
One study used deep learning to identify the
best anatomical landmarks for a total knee replacement,
helping surgeons plan their cuts and implant
placement more accurately. And we're also seeing
AI used on post -operative images to predict
future risks. There's a fascinating example of
the sources about using a convolutional neural
network, a CNN. which is great for image analysis.
That achieved 89 % sensitivity in classifying
total hip replacement patients for their risk
of dislocation just based on a single post -op
x -ray. Wow, 89%. That's very specific. What's
the actual practical value of predicting dislocation
risk like that from an x -ray? Well, if you can
reliably identify patients predicted to be low
risk, you could potentially tailor their post
-operative protocols. So not everyone needs the
same strict rules afterwards. Potentially, yes.
Instead of a one -size -fits -all approach, maybe
lower -risk patients could have fewer movement
restrictions. That might lead to faster or perhaps
less burdensome recoveries, while of course making
sure the high -risk patients get all the necessary
precautions. Right. It's about personalizing
that post -op care using data to assess risk.
Exactly. Data -informed risk assessment. So from
refining the actual implants to patient -specific
3D planning and even predicting post -op risks
during planning, AI seems to be really pushing
towards making surgical strategies more precise
and personalized. I think that's a fair summary,
yes. OK, let's shift gears a bit now. Moving
beyond the planning phase into the actual treatment
and then the post -operative journey, what role
are AI and other digital tools playing there?
Well, this is where we see AI really heavily
involved in predictive analytics. predicting
patient outcomes, predicting risks. The sources
detail quite a few models being developed and
tested for specific complications or maybe resource
needs. Like what kind of things? For instance,
predicting the need for blood transfusions after
a total knee replacement. That's discussed with
one machine learning model achieving an AUC of
0 .88. AUC area under the curve. That's a measure
of accuracy, right? Yes. Essentially how well
the model distinguishes between groups. In this
case, patients who will need a transfusion. versus
those who won't. 0 .88 is considered pretty good
performance. And these predictive models, they're
also being used for sort of operational efficiency
too, like predicting how long someone might stay
in hospital or if they might be readmitted. Oh,
absolutely. Predicting length of stay after procedures
like total hip replacement is a key area. Same
for predicting unplanned readmissions. Right.
And these predictions aren't just useful clinically.
The BOA source specifically highlights how predictive
analytics can really help improve hospital efficiency
and resource management. Forecasting demand,
managing patient flow better. Makes sense. They
even mentioned studies showing high accuracy
for predicting things like how long a specific
surgical case might take. And this predictive
capability also extends to quite serious complications.
Things like predicting periprosthetic joint infection
PJI or surgical site infections after knee replacements.
That allows for more targeted prevention efforts
in those identified as high risk. OK, predicting
objective clinical outcomes like infection or
length of stay, that makes intuitive sense. But
some sources mentioned predicting patient satisfaction.
That seems. Well, much harder to pin down and
predict. It is definitely complex. And the sources
acknowledge that even with technically successful
surgery, a significant chunk of patients, maybe
20%, sometimes even 30 % report they haven't
really improved, or they're actually dissatisfied
after a joint replacement. Wow, that high. It
can be, yes. What's interesting is that machine
learning models are being developed to try and
predict this dissatisfaction. And often, factors
like the patient's preoperative functional level
and their mental health status emerge as really
key influencers. So it's not just about the surgery
itself? Not entirely, no. It highlights that
satisfaction is multifaceted. One study mentioned
used a multimodal model pulling in various data
points and achieved an AUC of 0 .816 in predicting
dissatisfaction. But thinking about the data
used for these complex predictions, can we really
rely heavily on things like patient reported
outcome measures PROMs? Are they robust enough
on their own? That's a really important point
and the sources do caution against relying solely
on PROMs for prediction. They can suffer from
what's called a ceiling effect. Meaning? Meaning
patients who already have relatively high function
before surgery might not show a large measured
improvement on a problem score afterwards, even
if they personally feel the surgery was a huge
success for them. Ah, I see. The scale doesn't
capture their perceived benefit. Exactly. It
points to needing richer, perhaps more nuanced
data beyond just problem scores to get really
accurate predictions, especially for something
like satisfaction. And this whole area of predictive
power, especially around risk, it brings up quite
a significant ethical point that was raised in
the sources, doesn't it? Yes, it absolutely does.
It's a critical consideration. I mean, while
risk stratification is helpful for tailoring
care and allocating resources effectively, using
predictive models to label patients based on
their predicted outcomes or complication risk
carries a real potential for marginalization.
Yeah, so? Well, there's a genuine concern that
patients who are deemed too high risk by an algorithm
might be perhaps unfairly labeled as inoperable.
even if they might still stand to benefit significantly
from surgery. So the algorithm could effectively
deny them care. That's the fear. It raises very
challenging questions about equitable access
to care and what role algorithm should play in
fundamental clinical decisions. It's a complex
ethical landscape we need to navigate very carefully
as these tools get more sophisticated. Definitely
something to watch. Okay, beyond the predictive
AI side, what other digital tools are factoring
into the treatment and monitoring phases? Well,
robotic assisted surgery is obviously a key technology
that's integrated into treatment itself. It's
often highlighted for enhancing precision in
certain procedures, particularly mentioned in
spinal surgery examples in the sources. Right,
the robots. And then increasingly remote monitoring
tools are becoming important, especially postoperatively.
So things like wearables, external sensors, and
even the development of so -called smart implants.
Smart implants? That sounds very futuristic.
What sort of data are they actually providing?
Well... They're becoming much more of a reality.
The digitalization review we looked at mentions
smart implants being explored in areas like total
knee replacements or for monitoring how well
spinal fusion is healing. The idea is they can
provide real -time data on things like the biomechanical
load actually being placed on the implant. Inside
the body. Inside the body, yes. Or the status
of bone healing around it, potentially even detecting
early signs of the implant loosening or maybe
the formation of infection biofilms. Really granular
data. Fascinating. And what about the more familiar
things like wearables or external sensors? Yeah,
they're being used quite a bit to track patient
activity levels, their gait patterns, range of
motion during recovery. Wearables have actually
been validated for accurately monitoring these
sorts of parameters post -operatively for things
like knee and shoulder surgeries. And the main
benefit there is? The major benefit is enabling
effective remote recovery monitoring. It allows
clinicians to potentially tailor rehab plans
based on real data and maybe identify complications
earlier without needing so many frequent in -person
appointments. Right. Convenience and potentially
better oversight. That's the goal. OK. So all
of this you've got. imaging data, clinical notes,
sensor data from wearables, patient reports like
PROMs, maybe even data streaming from a smart
implant. That generates just an immense amount
of information, doesn't it? How do orthopedic
systems even begin to make sense of that, that
kind of fire hose of data? That is precisely
the major hurdle we touched on earlier. Data
integration. It's huge. And as the Digital Twins'
TED Talk highlighted, data from complex systems
like a human body undergoing recovery is often,
well, sparse in some ways, noisy in others, sometimes
indirect. And it often only gives you a snapshot
of now. It doesn't automatically tell you what's
going to happen next or why something is happening.
So just having more data isn't necessarily the
answer on its own. Not necessarily no. Yeah.
And this is where the concept of a digital twin
comes in as a potential maybe future direction.
A digital twin. Explain that a bit more. OK.
So the idea is combining predictive models. These
could be models based on physics. like how joints
biomechanically move, or models learned from
data using AI combining those models with something
called data assimilation. Data assimilation.
Yeah, it basically means continuously feeding
that model new real -time data from the actual
patient. So sensor data, maybe PROMs they submit,
perhaps even aspects of their lifestyle tracked
via an app. You feed that data in constantly.
To keep the model updated. Exactly, to keep the
digital model of that specific patient or maybe
their implant dynamic and current and reflective
of their reality. So you're building a constantly
updating digital replica of the patient's orthopedic
situation. That's a good way to put it, yes.
Applying this concept in orthopedics could potentially...
create a really comprehensive, integrated digital
representation of the patient. It could give
clinicians a much deeper, more dynamic understanding
than static records usually allow. Leading to
better predictions and more informed decisions,
presumably. That's the hope, but... And it's
a big but. Making this work requires establishing
reliable, connected clinical databases, the whole
big data infrastructure. And it needs pretty
unprecedented collaboration across different
disciplines. You need the clinicians, the data
scientists, legal experts for privacy and security.
And crucially, you need the patients involved,
too. And that brings us right back to the patient,
doesn't it? Many of these tools, especially the
monitoring apps, the data collection devices,
they require the patient to actively participate.
And we know getting people to consistently use
health care apps can be a real challenge. It
really, really can be. maybe insights from completely
different areas like the Duolingo TED Talk become
relevant. The language learning app, how's that
relevant? Well, the core idea discussed there
is about applying psychological techniques that
consumer tech uses very effectively. Things like
gamification, smart notifications, visual progress
trackers. Making it engaging. Exactly. Not to
make healthcare addictive like social media,
obviously, but to make... Using the necessary
tools, tracking your rehab exercises, reporting
your pain levels, allowing sensors to collect
data to make doing those things consistently
more engaging, maybe even rewarding. Sort of
making the broccoli taste like dessert, as the
speaker put it. Making the healthy, necessary
thing feel a bit more appealing. Precisely that
analogy. For orthopedics, it means thinking hard
about designing apps or integrating wearables
in a way that genuinely encourages patients to
stick with it, to track their progress, report
how they're feeling, allow the data collection.
Because the best remote monitoring tech in the
world is useless if the patient stops using it
after a week. Patient buy -in and continued engagement
is absolutely essential for the data flow these
advanced systems need. Couldn't agree more. It's
critical. So let's try and synthesize where we
stand then, according to these sources. The potential
for digital tech and AI in orthopedics looks,
well, immense. It seems to cover the entire patient
journey, right from that first contact all the
way through recovery. I think that's right. There's
clearly significant promise for improving efficiency,
for personalizing treatment pathways, enhancing
safety protocols, and potentially improving patient
outcomes, especially facing that rising demand
we talked about. But in it feels like a significant,
but running through the sources, many of these
applications are still very much in development
or maybe in the early stages of validation. Yes,
absolutely. Widespread routine adoption requires
more robust clinical evidence for many applications.
It needs standardized data protocols. We need
to overcome those significant data integration
challenges. We need to build trust and very importantly,
navigate those ethical considerations carefully
to ensure fair and equitable access and application.
Lots of potential, lots of work still to do.
Okay, fascinating insights. Let's finish off
with our lightning round quick questions, sharp
answers if you can. Based on these sources, what's
the single piece of digital tech that orthopedic
professionals should really be paying closest
attention to in the next, say, one to two years?
I'd say prediction models. Specifically, models
that move beyond using just one type of data
and start integrating clinical information, imaging
data, and potentially sensor data together for
more holistic prediction. Okay, integrated prediction
models. Beyond the technology itself, what's
the biggest non -technical challenge to successful
adoption of all these tools? For me, it has to
be establishing that robust, secure, and genuinely
integrated data infrastructure across different
parts of the healthcare system. It underpins
Everything else. The data infrastructure. Got
it. And finally, what's one small, maybe actionable
step an orthopedic professional listening could
take today based on what we've discussed? I think
a useful step would be to actively explore the
current state of data collection and accessibility
within their own clinical environment. Just understand
what data exists, how accessible it is, and identify
potential opportunities for maybe better integration
or better utilization of the data sources they
already have. Start by understanding your own
data landscape. Excellent. OK, let's quickly
recap the key takeaways from this deep dive for
you. Good idea. First, digital technology, especially
AI, it isn't just a nice to have anymore. The
sources really frame it as becoming essential
if we're going to manage the growing global demand
in orthopedics. Unavoidable, really. Second,
AI is showing practical promise right across
that patient journey, improving image analysis,
helping plan surgeries more precisely, and getting
better at predicting patient outcomes and risks.
Yes, definite progress there. Third, while digital
tools offer some screening potential, they currently
have clear limitations for standalone remote
diagnosis. That highlights the absolute need
to integrate multiple data sources, the exam
findings, labs, imaging, sensor data, not just
rely on one isolate. piece. Crucial point, that
integration. Fourth, making all this actually
work in practice requires building robust, secure
data infrastructure. And we have to tackle significant
non -technical hurdles like data integration
itself, validation, building trust, and ensuring
equitable access for all patients. Big challenges,
but necessary ones. And finally, for those tools
that need active patient participation, the apps,
the wearables, we really need to think about
how to genuinely engage users. Maybe borrowing
successful techniques from other digital fields
is key if these tools are going to deliver their
full value. Engagement is key, definitely. If
you found this deep dive valuable, please do
take a moment to rate and share it. It really
helps other professionals like you discover these
insights. Please do. And as you reflect on the
future of orthopedic care and the increasing
power of these digital tools, here's maybe something
to ponder. If AI gets really good at predicting
a patient's likelihood of satisfaction or their
risk of specific complications, how does that
fundamentally change the nature of the conversation,
that shared decision -making process that happens
between the doctor and the patient in the clinic
room? What does that look like?