Imagine a future, perhaps not too far off, where
orthopedic surgery, its precision, isn't just
about human skill anymore, but err, also about
artificial intelligence predicting complications
before they even happen. Or picture this, an
athlete's career -threatening injury being forecast,
you know, actively prevented rather than just
treated after the fact. Now, musculoskeletal
conditions affect a staggering 1 .71 billion
people globally. That puts immense strain on
healthcare systems. So this isn't just while
futuristic thinking, it's profound shift that's
actually already underway. Today we're taking
a deep dive into how artificial intelligence
is truly revolutionizing orthopedics and sports
medicine. How it's fundamentally shifting our
approach from, say, Reactive Treatment to Proactive
Personalized Prevention. Welcome to the Deep
Dive. This is the show where we unpack complex
topics, sift through the latest research, and
really try to extract the crucial insights from
all sorts of sources, all for you, our curious
listener. And joining us for this fascinating
deep dive is a true luminary in the field, Professor
Moet Meemem, as a consultant trauma and orthopedic
surgeon and a key contributor to significant
policy work on AI in orthopedics, including the
Orthopedic Research UK, that's O -R -U -K, policy
paper. He brings this unique blend of clinical
skill and, well, forward -thinking tech insight
to our chat. He's here to help us get to grips
with what's truly important in this, let's face
it, rapidly evolving landscape. Right, let's
kick things off with a few rapid -fire questions
just to set the stage for our deep dive today.
First question for you is this. What's the fundamental
shift AI brings to medical practice, particularly
in orthopedics? Is it more than just simple automation?
Are we talking artificial intelligence or perhaps
something more like augmented? That's a really
excellent way to and yes, it's a crucial distinction.
We're absolutely talking about augmented intelligence.
The core shift isn't really about AI replacing
skilled people. It's about amplifying their capabilities.
Think of AI as an incredibly powerful co -pilot,
maybe? A tool that performs tasks needing human
intelligence, you know, learning, reasoning,
spotting patterns, improving over time, but it
does it at a scale and speed humans just can't
match. It can sift through these huge complex
data sets in moments. That lets clinicians make
more informed decisions, boosts diagnostic accuracy,
streamlines workflows. We've seen this quite
clearly in studies, actually. Take mammography,
for instance. Physicians working with AI achieve
better diagnostic outcomes than either the AI
or the physician working alone. So yes, it's
definitely about making the human expert even
better, not about replacing them. The idea of
amplification, it's really powerful, isn't it?
Building on that, then, from your perspective,
where are we seeing the most surprising, perhaps,
and impactful early wings for AI, either in orthopedic
patient care or, say, sports injury prediction?
What's genuinely getting you excited right now?
Well, the early ones are genuinely impactful,
yes, and often quite surprising in just how precise
they can be. In orthopedics, The diagnostic capabilities
powered by AI are really quite striking. Imagine
AI algorithms, especially the deep learning ones,
assessing conditions like osteoarthritis from
x -rays with an aptitude that's comparable to,
or sometimes even better than, a doctor with
a decade's experience. That means faster, more
consistent diagnoses for patients. Beyond that,
we're seeing real strides in predicting patient
outcomes. Identify who is at high risk for complications
like parapathetic joint infection, or even predicting
which patients might feel dissatisfied after
their surgery. This allows for proactive intervention,
doesn't it? Not just reacting after something
goes wrong. And in sports, while that shift towards
proactive injury prevention is frankly a game
changer, AI acts like an early warning system.
It analyzes these complex multidimensional data
sets. wearables, biomechanics to predict muscle
injuries, manage athlete workloads. You mentioned
Health First earlier, they've deployed what,
17 predictive models using AI? Generated nearly
a thousand custom machine learning features to
improve health outcomes. That really shows its
power. That is remarkable. So, OK, if AI holds
all this promise, what's the single biggest hurdle?
What's stopping its widespread adoption and,
you know, realizing its full potential in this
field today? Without a doubt, it really boils
down to data, specifically its quality, its diversity
and crucially, its accessibility. Look, AI models
are only as good, only as robust as the data
they're trained on. That's fundamental. And the
reality is much of our health care data is still
Well, it's uncaptured or unstructured. It's varied,
stuck in different silos across different systems.
This makes it incredibly expensive and time -consuming
to clean it up, standardize it, integrate it
into formats that AI can actually use. And often,
we lack that really comprehensive data, don't
we? Especially the contextual stuff, social factors,
lifestyle choices, daily activities, all vital
for accurate prediction, but often missing. On
top of that, you've got the sensitivity of patient
data. It's highly regulated and rightly so for
privacy, but that does create significant barriers
for AI model training and deployment. That's
why initiatives to standardize data collection
and ensure secure ethical access are just so
critical. That paints a very clear picture of
that foundational challenge. Okay, now let's
unpack this core concept a bit more. You mentioned
augmented intelligence rather than just... artificial.
What does that actually mean for how clinicians
and sports scientists will work day to day? It
means a pretty profound transformation in how
we interact with technology, I think. At its
heart, artificial intelligence, AI, it refers
to computational techniques enabling machines
to do tasks that usually need human intelligence.
Things like learning, reasoning, solving problems,
spotting patterns, and crucially, the ability
to get better iteratively as they get more data.
Within AI, we've got these specific powerful
tools. There's machine learning, or ML, where
algorithms learn from data without being explicitly
programmed for every single scenario. Then deep
learning, DL, which uses these layered neural
networks to process huge amounts of complex data.
It's particularly good at image and pattern recognition.
And natural language processing, NLP, which helps
computers understand, interpret, and even generate
human language. Now the augmented intelligence
perspective, which bodies like the American College
of Physicians strongly advocate, views AI not
as a replacement for human expertise, no, but
as a powerful enhancement, a tool, and the evidence
consistently backs this up. When physicians work
with AI like reviewing mammograms for breast
cancer, the diagnostic outcomes are demonstrably
better compared to either working alone. It's
about leveraging AI's knack for processing vast
information, spotting subtle patterns, allowing
the clinician to focus their highly trained judgment
where it truly matters. It should lead to a more
efficient, more precise healthcare journey overall.
That's fascinating. So it's really about collaboration,
isn't it? Where the human brain and the machine's
processing power kind of create a sum greater
than their parts. Can you give us some specific,
tangible examples of how this augmentation plays
out in a clinic or a sports science setting?
Where's it making a real difference right now?
Absolutely. Let's think about something as fundamental
as clinical documentation. It takes up so much
time. Digital scribes, like Dax Copilot from
Microsoft, they're AI -powered, voice -enabled
solutions. They basically listen in during patient
interactions and automatically generate comprehensive
clinical notes. Now, this isn't just about saving
time, though that's significant. Studies show,
I think it was 70 % of physicians using it reported
better work -life balance, less burnout, and
perhaps even more importantly, 93 % of patients
felt their doctor was more personable, more engaged,
because they weren't constantly looking at a
screen and typing. It frees clinicians up to
actually connect with their patients rather than
wrestling with the admin side of things. That's
a huge impact on the human side of medicine.
Exactly. And we see this powerful augmentation
in diagnostic fields, too. Radiology, pathology,
ophthalmology, dermatology. Here, AI systems
can analyze images, identify anomalies, or subtle
patterns the human eye might miss, or maybe just
take much longer to spot. Again, it's not saying
the human is redundant. Far from it. The AI provides
a powerful second opinion maybe, or a rapid first
pass, flagging up areas of concern for the human
expert's focused review. Even seemingly mundane
admin tasks, patient scheduling, resource optimization
in a hospital, they're benefiting from AI too,
making things run smoother, more efficiently.
There's a quote from Dominique Rothenflue, a
highly respected orthopedic surgeon at Oxford
University Hospitals. He read it quite profoundly.
Machines may not necessarily replace physicians,
but physicians making use of AI will at some
point replace those not using it. And the sale
of the other. Now, that's not meant as a threat.
It's more of an imperative, really, for professionals
to adapt, to integrate these tools into their
practice, to ensure they stay at the forefront
and keep delivering the highest standard of care.
That is a powerful statement about the future
of practice. Okay, so AI is clearly reshaping
the before picture diagnosis planning. Let's
delve into that a bit more. Before a patient
even gets near an operating theater or an athlete
steps onto the pitch, AI is already at work.
Can you walk us through some of these really
critical preoperative applications? Yes, the
preoperative phase is, er, tremendously fertile
ground for AI. It's transforming how we diagnose,
predict outcomes, plan interventions. Let's start
with imaging and diagnosis. Traditionally, diagnosing
something like osteoarthritis, OA, from x -rays,
has been manual, often subjective, depends who's
looking, right? Now, AI algorithms, especially
these convolutional neural networks, CNNs, they're
basically AI systems really good at seeing and
interpreting patterns in images, a bit like our
own brain's process vision, they're automating
this. For instance, one significant study developed
a CNN model for hip OA diagnosis using hundreds
of radiographs. And what they found was compelling.
This AI model showed diagnostic aptitude comparable
to a physician with 10 years clinical experience.
10 years? Wow. Exactly. So what that means for
you, the patient, is potentially faster, more
consistent, highly accurate diagnoses. Similarly,
AI can accurately classify the severity of knee
OA using the standard Kellgren -Lorentz, or KL,
greeting system. It offers better reproducibility
than manual evaluation. Some newer approaches,
deep Siamese CNNs, can even give probability
distributions for ambiguous cases. That's incredibly
useful for clinical decisions, helping clinicians
gauge the AI's confidence level. But there's
a key challenge here, one we absolutely must
address, generalizability. Algorithms trained
mainly on data from one hospital might struggle
with different labeling systems or radiation
protocols elsewhere. Right, so it might work
brilliantly in one place, but not another. Precisely.
So for broad use across different hospitals,
different clinics, these models need really diverse,
extensive data sets for training. That's crucial.
That diverse data point keeps coming up, doesn't
it? Okay, so beyond just diagnosing what's happening
now, how is AI helping us predict what's likely
to happen for a patient's future? Yes, this is
where AI truly shines, I think. in its proactive
capability, moving us from just reacting to treatment
towards actually having some foresight. Machine
learning and deep learning models are now significantly
outperforming traditional statistical methods
in predicting, say, the structural progression
of OA, or identifying individuals at high risk
for needing a total joint replacement. a TJR.
For example, one study developed a deep learning
program using baseline knee x -rays to predict
medial joint space loss. That's a key sign of
OA getting worse. Their model achieved a significantly
higher AUC score. That's a metric for how well
a model distinguishes between groups, like those
who will progress versus those who won't. Their
AUC was, I think, 0 .799 compared to 0 .660 for
traditional models. That indicates a much better
predictive ability. Others found deep learning
could predict the need for total knee replacement,
TKA, just from radiographs, with pretty impressive
sensitivity and specificity. And that's not just
academic, is it? That has real -world implications.
Absolutely. This ability to predict lets us potentially
foresee osteoarthritis progression much earlier.
It enables proactive interventions that might
slow the disease down for a patient, or at least
help them prepare for what's likely coming. It's
about being prescriptive, not just descriptive,
giving people options earlier. So it's not just
about what's happening now, but what's likely
down the road. And how does that translate into
improving the patient experience? Especially
given, as you said, quite a few patients report
dissatisfaction even after a technically successful
surgery. Exactly. Patient satisfaction is a critical
challenge. It's quite sobering, really, that
20 to 30 percent of patients report no improvement
or even dissatisfaction after a total joint replacement.
So AI models are now being developed specifically
to predict this dissatisfaction. These models
can identify influential factors, things like
preoperative functional scores, maybe mental
health status allowing us to spot high -risk
patients beforehand. One study, for instance,
trained models that achieved strong AUCs, around
.81, in predicting knee -away dissatisfaction
two years after surgery. This means we can potentially
intervene with targeted preoperative education,
perhaps health optimization programs, or even
guide patients towards non -surgical options
earlier. That could reduce unnecessary treatments
and significant costs later on. It's about tailoring
the care pathway much more to the individual.
That sounds much more patient -centric. It aims
to be. And AI also excels at risk stratification
of various complications, making surgery safer
overall. For instance, predicting the need for
blood transfusion after TKA1 and ML model did
this with an excellent AUC of 0 .880 using just
six readily available preoperative factors. Or
for periprosthetic joint infection, PJI, which
is a really severe complication. An artificial
neural network identified obesity as a stronger
predictor than previously thought. And ML systems
have even outperformed traditional fixed criteria
for diagnosis, offering more adaptive, transparent
models, helping clinicians diagnose faster and
more accurately. Length of stay, LOS, that's
a major cost driver in hospitals. AI models can
identify key predictors age, BMI, operation time,
blood results. This allows for better resource
allocation, more efficient hospital management.
Better for everyone, really, but... And this
is really important, but a significant ethical
concern arises here. These powerful predictive
methods could inadvertently marginalize patients
flagged as too high risk. Ah yes, the risk of
creating a sort of uninsurable category but for
surgery. Potentially yes. We must be incredibly
vigilant to ensure these tools are applied equitably,
that no vulnerable individual is excluded from
necessary treatment just because of an algorithm's
prediction. The goal must always be to improve
access and outcomes for all. That's a really
crucial point about equitable access, ensuring
these powerful tools enhance rather than limit
patient care. Okay, moving beyond diagnostics
and risk, how is AI influencing these sort of
nuts and bolts of preparing for surgery? Things
like surgical planning and even implant design.
Yes, this is another area where AI is driving
some incredible innovation. AI software is now
directly informing implant design, positioning,
and 3D templating. All critical for successful
outcomes. For example, researchers have applied
machine learning to optimize short stem implant
design and hip replacements, aiming to reduce
something called stress shielding, which can
weaken the bone over time. Others have used deep
learning to estimate patient -specific adjustments
for optimal landmark use in total knee arthroplasty
planning, ensuring a much more precise fit for
that individual patient. There's also AI -based
3D preoperative planning software, like AI -HYPE
for total hip replacement, THA. Studies found
it offered significantly more accurate surgical
planning and component sizing compared to traditional
2D templating. And it saves surgeons valuable
time by giving these highly precise visualizations
of the anatomy. usually need CT scans, which
means more radiation and cost. That's often been
the case, yes, but future AI applications promise
to mitigate this. Some researchers, for instance,
have developed an innovative AI program that
can convert standard 2D X -rays into highly accurate
3D bone models. This could potentially reduce
the need for those costly higher radiation CT
scans, making this advanced planning much more
accessible. Another fascinating application is
automatic implant identification. With so many
THA procedures done now, correctly identifying
implant types for potential revisions is vital,
but it can be tricky and time consuming with
the sheer number of different implants out there.
So, researchers developed a deep learning tool
for automatic THA implant detection, trained
on over 240 ,000 radiographs. It achieved impressive
accuracy, 98 .9 % internal, 97 % external for
femoral components across different x -ray views.
And a unique, really vital aspect of their model
is it incorporates uncertainty quantification,
basically. It gauges its own confidence level
in outlier detection, flagging data points that
might skew results. These features are absolutely
vital for enhancing trust in the model and supporting
its potential for widespread clinical use. gives
surgeons confidence in what the AI is telling
them. OK, so AI is clearly reshaping the before
picture with remarkable precision. But what happens
during surgery? And then crucially, in that often
overlooked recovery phase, how is AI stepping
in there? That's an excellent question because
AI's utility definitely extends significantly
into the intraoperative and postoperative periods
too. During surgery, we're seeing this rapid
integration of AI into quite sophisticated tools.
Think augmented reality, AR -based navigation
systems, AI -assisted arthroscopic procedures,
and increasingly advanced robotic surgery. Robotic
surgery, already known for its minimally invasive
approach, brings greater precision. That can
lead to less blood loss, less pain for the patient,
and typically shorter recovery times. AI's role
here isn't just controlling the robot's arm.
It refines surgical planning by analyzing huge
amounts of data from previous successful surgeries.
And it provides real -time feedback during delicate
procedures like bone cutting or soft tissue resection.
This helps ensure remarkably consistent, reproducible
results, which is key for patient safety. getting
the best possible outcome. It sounds incredible,
but there must be challenges to adopting this
everywhere. Oh absolutely. As with any advanced
tech, there are hurdles. The financial investment
for robotic gear and the consumables is substantial.
System compatibility can be limited to specific
implant types, and crucially, extensive ongoing
training for surgeons and the whole theater staff
is absolutely essential for safe and effective
integration. It really requires a dedicated commitment
from healthcare systems. It's not just plug and
play. It sounds like incredible progress, but
the idea of AI assisting during surgery, it does
raise crucial questions, doesn't it? About human
expertise versus relying on the machine. How
do we ensure human intuition and skill remain
paramount? You've hit on a really critical point
there. A very valid concern is what we sometimes
call automation bias or over -reliance on AI.
If surgeons become overly dependent on the technology,
there's a genuine risk that their foundational
human intuition, their clinical judgment, even
their manual skills could diminish over time.
And if the technology fails or encounters an
unexpected situation the AI hasn't seen before,
that over -reliance could lead to severe consequences.
We can draw parallels perhaps with the airline
industry's use of autopilot. It's incredibly
efficient for routine flight, yes, but pilots
must remain highly skilled, capable of taking
manual control in emergencies. This also fundamentally
changes surgical training. It needs to evolve
to include not just manual skill, but also proficiency
in operating, interpreting, and critically understanding
these advanced machines. It's about building
a robust human AI team, not just delegating tasks
to a black box. That makes sense. Building a
team, not just relying on a tool. And what about
after the surgery, in the recovery phase? Right.
Postoperatively, AI is revolutionizing rehabilitation
and monitoring, primarily through remote patient
monitoring. The Internet of Things, IoT, and
mHealth mobile health solutions. They integrate
AI into wearable devices, sophisticated sensors,
smartphone apps, collecting vast amounts of real
-time patient data from their own home. Clinicians
can then remotely monitor a patient's rehab progress,
stepping in quickly if milestones aren't met
or if complications seem to be rising. For instance,
these innovations have shown real value in reducing
opioid use after surgery and improving adherence
to anticoagulation drugs, which are vital for
preventing serious blood clots. There was a study
developing motion -based machine learning software
for remote total knee arthroplasty monitoring.
It used a simple knee sleeve with Bluetooth sensors
transmitting data to a phone app. It gave clinicians
invaluable insights into the patient's mobility,
how well they were doing their exercises, and
could even flag up early signs of complications.
And crucially, patients found it really motivating,
engaging. It transforms how we oversee recovery
from just reactive clinic visits to a more proactive
continuous feedback loop directly from their
home. That sounds much more empowering for the
patient too. It really can be. And finally, AI
extends its powerful image analysis skills to
complication screening via imaging post -operatively
as well. It can detect early signs of implant
loosening or determine dislocation risk, often
with far greater accuracy and efficiency than
traditional methods. That allows for prompt intervention
before a problem really escalates. Prosthetic
loosening, for example, it's a leading cause
of arthroplasty failure, accounts for 30 % of
knee revisions, a huge 55 % of hip revisions.
It's a massive clinical and economic burden.
One group developed a fully automated CNN algorithm
to detect mechanical loosening of hip implants
from plain x -rays, reporting high sensitivity
around 91 .6%. This doesn't just supplement the
surgeon's decision -making, it also frees up
their valuable time for more patient -focused
activities. Similarly, hip dislocation after
replacement is a significant challenge, costs
a lot too. Another group developed a CNN, trained
on over 92 ,000 x -rays, to classify patients
for dislocation risk with about 89 % sensitivity.
Identifying low -risk patients with high confidence
could mean fewer post -operative restrictions,
fewer follow -up visits. That eases the burden
significantly on both patients and the healthcare
system. So, okay, broadening out from the clinical
setting. What does all this mean for athletes
from the weekend warrior maybe right up to the
elite professional? How is AI truly enabling
this shift from just reacting to injuries to
actively preventing them, transforming sports
medicine? Yes, this is a truly transformative
area. AI fundamentally shifts the whole paradigm
in sports medicine from reactive treatment to
proactive prevention. Musculoskeletal injuries
globally are a huge burden in sports, aren't
they, impacting careers, well -being. AI, mainly
through advanced machine learning and deep learning,
is designed to analyze these incredibly complex
multi -dimensional data sets. We're talking data
from high -tech wearables, detailed biomechanics
assessments, performance metrics, speed, power,
and even subtle psychological factors like sleep
quality or stress levels. This rich data lets
AI identify subtle patterns, risk factors that
traditional methods just relying on human observation
might easily miss. It's about getting ahead of
the curve, predicting vulnerability before an
injury actually occurs. So it's like seeing the
invisible signs. In a way, yes. In team sports,
AI models are already widely used to predict
muscle injuries, meticulously manage workloads
in demanding sports like football, basketball.
Think about high -contact sports rugby American
football. Here, AI precisely monitors collision
data to track an athlete's recovery from concussions,
providing early warnings based on cumulative
impacts over a season. Studies clearly highlight
AI's role as an early warning system, spotting
injury risks before they even manifest as a serious
problem. This proactive approach lets coaches,
medical staff, implement timely interventions,
adjust training loads, rest an athlete, potentially
saving their season, maybe even their career.
And what about individual sports? Where the demands
might be different? The application is equally
groundbreaking there. AI monitors real -time
biomechanical data, often from sensors embedded
right into clothing or equipment. It detects
subtle signs of fatigue or poor technique that
could predispose an athlete to injury. There
was the first prospective longitudinal cohort
study using machine learning to identify specific
risk factors for running -related injuries that's
revolutionizing prevention strategies for runners.
We're also seeing truly adaptive technologies.
Like mechatronic ski bindings using AI for real
-time knee protection in alpine skiing, they
instantly adjust based on forces to prevent injury.
That's incredible stuff. In sports like cycling,
swimming, tennis, gymnastics, AI analyses precise
biomechanics to prevent overuse injuries. CNN's
analyzing high dimensional wearable sensor data
can detect risky movements that would otherwise
just go completely unnoticed. And it helps with
getting back after an injury too. Absolutely.
AI plays a critical role in optimizing return
to play protocols. It doesn't just predict, it
prescribes. By creating personalized rehabilitation
plans based on an individual athlete's unique
data, AI acts as both a predictive and prescriptive
tool. It meticulously monitors recovery metrics,
tracks cumulative impacts, especially vital and
contact sports, to minimize the risk of re -injury
and support long -term athlete health. A prime
example in action is MAI Motion's AI applications.
They offer precise diagnostics, customized rehab
pathways, predictive analytics for athlete recovery,
and they've showed significant reductions in
recovery time, getting athletes back to peak
performance faster, safer. Similarly, Health
First, using closed -loop tech, deployed the
those 17 predictive models, nearly a thousand
customized machine learning features. It just
shows the immense value AI can drive by speeding
up insights into clinical workflows, improving
health outcomes across a population. It truly
heralds a new era of data -driven athlete recovery
and health management, making prevention not
just a goal, but a tangible reality. It's clearly
transformative. But as you've hinted, no technology
like this comes without challenges. While the
promise is clear, what are the significant hurdles
that really need addressing for AI to embed itself
seamlessly and responsibly into health care and
sports, especially thinking about those ethical
nuances? Indeed, there are significant hurdles.
They mainly cluster around data, ethical considerations,
and, well, operational realities. Firstly, data
quality diversity. As I mentioned earlier, AI's
effectiveness is entirely dependent on having
substantial, diverse, high -quality datasets.
The reality is, much healthcare data remains
uncaptured, unstructured, varied, stuck in silos.
It's hugely costly and time -consuming to clean,
standardize, integrate it for AI. Crucially,
comprehensive data, including those social determinants
of health, lifestyle choices, daily activities
vital for accurate prediction, are often systematically
lacking. Our own RU case survey found I think
76 % of the MSK community agrees it's difficult
to access and use patient data at scale, often
because of how data protection rules are interpreted.
For instance, researchers have been told to get
updated written consent from every single patient
for long -running datasets, even if generic consent
was given and data anonymized. That can render
vital research completely unviable. That sounds
like a major bottleneck. It really is. And it
leads directly into the complex ethical and legal
considerations. A major worry is algorithmic
bias. If AI systems are trained on data that
isn't representative or reflects historical biases,
they can inadvertently generate unequal outcomes
based on race, gender, ethnicity. This could,
unfortunately, lead to disparities in care quality,
accessibility for certain patient groups. There
are urgent calls for much greater transparency
in how these algorithms are built and robust
oversight to tackle this threat. Data privacy,
patient confidentiality are, of course, paramount.
Entering protected or identifiable patient info
into public AI systems, that's a clear violation
of ethical legal obligations. We need robust
ethical frameworks to guide responsible AI deployment,
always prioritizing patient dignity, autonomy,
privacy rights, Transparent, accessible, informed
consent processes are also vital, especially
for people with limited English proficiency,
ensuring patients fully understand how their
data will be used. That has to align with rules
like GDPR and NDP. The transparency aspect feels
particularly crucial doesn't it? If a doctor
can't actually explain how an AI reached a conclusion,
That surely arraigns trust. Absolutely. There's
a real risk of this automation bias or over -reliance
on AI we talked about, where over -dependence
could diminish human intuition, clinical skills.
If the tech fails or hits a unique situation
the AI wasn't trained for, that over -reliance
could have severe consequences. Clinicians need
to understand the underlying logic, not just
blindly accept if the model says so. This lack
of transparency, the black box problem, is a
significant barrier to trust and widespread adoption.
Clinicians must be able to critically evaluate
AI recommendations, just like any other diagnostic
test. And the operational side, beyond data and
ethics. Yes, several operational challenges too.
The inconsistent application of AI tech, due
to a lack of clear unified regulations, creates
uncertainty for developers and healthcare providers
alike. While AI can create new jobs, there's
also potential for job displacement as AI takes
on tasks humans used to do. That needs proactive
planning, retraining initiatives. There's a pressing
need for systemic oversight in AI research and
development generally to prevent exploitation,
ensure equitable outcomes for all patient demographics,
and fundamentally, even the most sophisticated
AI can't function without high -quality ground
truth data. That means accurate labeling, tagging
of orthopedic images by human experts is absolutely
fundamental for training reliable AI models.
That often requires significant human effort
and expertise right at the start. These are definitely
significant challenges, but you also mentioned
orthopedics seems uniquely positioned somehow
to overcome them. What makes this field such
fertile ground for AI and what are the key steps
needed to move forward and really unlock its
potential? Orthopedics is indeed uniquely fertile
ground for this kind of technological innovation.
It tends to have clear, well -defined pathways
for common diseases, which helps with data analysis,
and it's inherently a highly technical field,
always embracing innovation in devices, procedures,
As Professor Xavier Griffin from Queen Mary University
of London noted, orthopedics is always at the
vanguard when it comes to using large data sets
and embracing new tech. Yet, despite this natural
fit, it often gets overlooked in broader AI health
care discussions. A landmark NHSX report, for
instance, didn't even mention orthopedics. Cancer,
drug development often get the headlines, the
investment. And that, I believe, is a missed
opportunity because even small gains in orthopedics
can lead to better outcomes for huge patient
populations globally. just given the sheer volume
of musculoskeletal conditions. So how do we capitalize
on that potential? What's being done? Well, current
initiatives are crucial. Orthopedic Research
UK, ORUK, is playing a key role here, encouraging
AI application through education, strategic research
investment, pushing for data standardization,
fostering networking across the field, really
calling on the whole MSK community to get involved.
A shining example of data -driven success already
is the Getting it Right First Time program, GRLFT.
It leverages data sharing across orthopedic departments
to identify best practices, improve care, and
GRFT has led to remarkable results, reduced revision
rates, shorter hospital stays by a fifth, releasing
over 368 ,000 bed days, and has delivered staggering
NHS savings 696 million over just five years.
AI can further inform decision -making within
programs like GRFT by spotting those nuanced
patterns, helping remove existing biases in resource
allocation or patient care pathways. give us
some more concrete examples how AI is already
making a difference perhaps beyond the research
labs in everyday orthopedic practice patient
lives. Certainly. predictive modeling, for instance.
It's already identifying hip replacements at
risk of failure. And this isn't just stats. AI
is spotting subtle changes on x -rays missed
by the human eye, proving more accurate even
than experienced clinicians sometimes. This helps
flag problems early, preventing catastrophic
failures, yes, but also potentially saving the
NHS tens of millions by reducing the need for
routine screening of joints that are highly unlikely
to fail anyway. AI is also assisting with swift,
accurate classification of fractures. That's
vital given the significant backlog of unreported
x -rays in the UK, estimated over 300 ,000 waiting
more than 30 days. AI can help triage these,
ensure urgent ones get reviewed quickly, reducing
delays in care. That backlog figure is shocking.
It really highlights the need. Oro UK has even
taken an equity stake in radii devices, a promising
startup using AI and biomechanical modeling to
design better fitting prosthetic sockets for
amputees. The aim is to drastically reduce the
number of clinical visits needed for a comfortable
fit, potentially from up to nine visits down
to far fewer. That dramatically improves patient
comfort, speeds up their rehab. And then there's
the widespread use of wearables in patient apps,
things like Get You Better, Good Boost, the Versus
Arthritis AVA Chatbot. They harness AI to provide
personalized self -management support at home.
This reduces the need for GP appointments, physio
referrals, even medication by empowering patients
with tailored advice exercises. Our Oreo case
survey showed an overwhelming 91 % of the MSK
community agrees passive continuous monitoring
can significantly aid post -treatment recovery,
underscores the value of these AI tools. Those
are really impressive practical applications
showing direct benefits. So looking ahead now,
what are the key ingredients needed to accelerate
AI's integration, maximize its impact across
orthopedics, sports medicine? Several crucial
elements are needed for us to really unlock AI's
full potential, I think. Firstly, Continued enhancements
in machine learning and natural language processing.
Advancements in ML algorithms mean faster, more
accurate diagnoses from complex medical images,
X -rays, MRIs, CTs. NLP will streamline data
access, translate vast amounts of unstructured
medical jargon into actionable insights, significantly
improve patient management through speech recognition,
efficient record processing, huge operational
efficiencies there. Secondly, interdisciplinary
collaboration. Absolutely crucial. We need seamless
cooperation between clinicians, data scientists,
engineers, policymakers, and importantly, patients
themselves. As Dr. Justin Green in Orthopedic
Registrar rightly said, we need to involve genuine
data scientists. They bring skills clinicians
no matter how tech savvy cannot replicate. This
collaboration ensures AI solutions are both technically
sound and clinically relevant. Makes sense. You
need both sides of the coin. Exactly. Thirdly,
there's an urgent need for AI education. Our
ORR UK survey highlighted that startling figure,
75 % of MSK professionals feel they lack the
knowledge to apply AI and big data effectively.
We need structured, comprehensive training tailored
to MSK professionals. ORR UK is actively working
on developing such a program to upskill the workforce.
Fourth, research investment. critically lacking.
71 % of the MSK community agrees there's insufficient
funding for AI research in MSK health. We must
strategically fund innovative projects using
AML to reduce MSK pain, improve function, cut
costs, and fundamentally enhance quality of life
for millions. Fifth, data standardization. Paramount,
championing better data standards for MSK research,
including improved reporting of AI studies, addressing
eligibility criteria, ground truth data, data
set diversity model details, the need for rigorous
external validation, larger sample sizes. This
will help counter biases from narrow data sources,
making AI models more robust, more equitable.
So better data, better reporting. Precisely.
And finally, we need proactive regulatory frameworks,
ones that strike that crucial balance between
fostering innovation and ensuring patient protection.
They must ensure AI advancements contribute positively
to public health without infringing on individual
rights, and crucially, longitudinal studies and
validation across different sports environments,
diverse athlete groups. That's absolutely essential
to improve accuracy, generalizability, and finally
bring these groundbreaking innovations from promising
research into widespread, impactful clinical
practice. Right, now for our lightning round.
Quick, sharp insights. What's one AI technology
you believe is poised to make the biggest immediate
impact in orthopedics in, say, the next 12 to
18 months? I'd have to say AI -powered diagnostic
imaging for faster, more accurate detection,
coupled with predictive analytics for early intervention,
particularly around complications or predicting
outcomes. That's where the immediate wins are.
Okay. And for an aspiring athlete, maybe even
a weekend warrior like me, looking to leverage
AI for personal injury prevention, what's one
practical, actionable step they could take today?
I'd say start by embracing AI -powered wearable
technology if it's accessible. Use devices that
monitor your real -time biomechanics, your workload.
Look for apps or platforms offering personalized
feedback based on that data. Companies like MAI
Motion show how using this data for customized
rehab, predictive analytics can really cut recovery
time and prevent injuries. It gives you a powerful
edge. Good advice. Finally, for any healthcare
professional or sports scientist out there feeling,
well, maybe a bit overwhelmed by the sheer pace
of AI change, what single piece of advice would
you offer them? I'd say Embrace AI as augmented
intelligence, not as a replacement. Focus on
fostering that interdisciplinary collaboration
with data scientists and commit to continuous
learning, especially understanding AI ethics
and how to critically interpret AI -generated
data. Your human expertise remains invaluable.
AI just makes it potentially more powerful. OK,
brilliant. So let's try and crystallize our key
takeaways from today's Deal Dive. Firstly, artificial
intelligence is fundamentally an augmentative
tool. It's designed to... enhance not replace
the expertise of clinicians and sports scientists
leading to greater precision, greater efficiency
in care. Secondly, AI's applications are vast.
They span the entire patient journey, the athlete
life cycle from predictive diagnostics, personalized
treatment planning, right through to real -time
monitoring and advanced rehab. Thirdly, while
the benefits are immense, successful integration
really hinges on diligently tackling those crucial
challenges, ensuring access to high -quality,
diverse data, navigating complex ethical and
privacy concerns, and establishing clear, transparent
regulatory frameworks. Fourthly, orthopedics
in particular stands out as uniquely fertile
ground for AI innovation. It offers immense potential
for significant cost savings and vastly improved
outcomes for huge patient populations, provided
there's continued strategic investment and targeted
education. And finally, the future of AI in medicine
and sports demands unwavering interdisciplinary
collaboration and a steadfast commitment to integrating
this technology ethically, responsibly, ensuring
it always complements and elevates human expertise.
If you found this deep dive valuable today and
gained some new insights, please do consider
rating and sharing it with a colleague or a friend
who might also benefit from these discussions
on AI's impact in healthcare and sports. Thank
you so much for illuminating this incredibly
dynamic field for us today. It's been truly insightful.
And thank you, our listener, for joining us on
the deep dive. Do subscribe to make sure you
don't miss our next exploration into the fascinating
world of knowledge and insight. Now, as AI becomes
ever more intertwined with our health, just think.
How might our understanding of human performance,
of recovery, evolve when every subtle change,
every nuanced response can be quantified, understood
by algorithms? What new frontiers will this precise
understanding unlock for human potential?