Latest / Deep Dive Ortho — Orthopaedic Surgery, AI & Digital Health Podcast. / AI's Future in the Patient Journey

Transcript

AI's Future in the Patient Journey · Deep Dive Ortho — Orthopaedic Surgery, AI & Digital Health Podcast.

0:00
Imagine a future, perhaps not too far off, where
0:03
orthopedic surgery, its precision, isn't just
0:05
about human skill anymore, but err, also about
0:08
artificial intelligence predicting complications
0:10
before they even happen. Or picture this, an
0:14
athlete's career -threatening injury being forecast,
0:16
you know, actively prevented rather than just
0:18
treated after the fact. Now, musculoskeletal
0:20
conditions affect a staggering 1 .71 billion
0:24
people globally. That puts immense strain on
0:26
healthcare systems. So this isn't just while
0:28
futuristic thinking, it's profound shift that's
0:31
actually already underway. Today we're taking
0:33
a deep dive into how artificial intelligence
0:36
is truly revolutionizing orthopedics and sports
0:38
medicine. How it's fundamentally shifting our
0:41
approach from, say, Reactive Treatment to Proactive
0:43
Personalized Prevention. Welcome to the Deep
0:46
Dive. This is the show where we unpack complex
0:48
topics, sift through the latest research, and
0:50
really try to extract the crucial insights from
0:52
all sorts of sources, all for you, our curious
0:54
listener. And joining us for this fascinating
0:56
deep dive is a true luminary in the field, Professor
0:59
Moet Meemem, as a consultant trauma and orthopedic
1:01
surgeon and a key contributor to significant
1:03
policy work on AI in orthopedics, including the
1:07
Orthopedic Research UK, that's O -R -U -K, policy
1:10
paper. He brings this unique blend of clinical
1:13
skill and, well, forward -thinking tech insight
1:15
to our chat. He's here to help us get to grips
1:17
with what's truly important in this, let's face
1:19
it, rapidly evolving landscape. Right, let's
1:22
kick things off with a few rapid -fire questions
1:24
just to set the stage for our deep dive today.
1:27
First question for you is this. What's the fundamental
1:30
shift AI brings to medical practice, particularly
1:32
in orthopedics? Is it more than just simple automation?
1:35
Are we talking artificial intelligence or perhaps
1:37
something more like augmented? That's a really
1:39
excellent way to and yes, it's a crucial distinction.
1:42
We're absolutely talking about augmented intelligence.
1:46
The core shift isn't really about AI replacing
1:48
skilled people. It's about amplifying their capabilities.
1:52
Think of AI as an incredibly powerful co -pilot,
1:54
maybe? A tool that performs tasks needing human
1:57
intelligence, you know, learning, reasoning,
2:00
spotting patterns, improving over time, but it
2:03
does it at a scale and speed humans just can't
2:05
match. It can sift through these huge complex
2:08
data sets in moments. That lets clinicians make
2:11
more informed decisions, boosts diagnostic accuracy,
2:14
streamlines workflows. We've seen this quite
2:16
clearly in studies, actually. Take mammography,
2:18
for instance. Physicians working with AI achieve
2:20
better diagnostic outcomes than either the AI
2:22
or the physician working alone. So yes, it's
2:25
definitely about making the human expert even
2:27
better, not about replacing them. The idea of
2:30
amplification, it's really powerful, isn't it?
2:32
Building on that, then, from your perspective,
2:34
where are we seeing the most surprising, perhaps,
2:37
and impactful early wings for AI, either in orthopedic
2:40
patient care or, say, sports injury prediction?
2:43
What's genuinely getting you excited right now?
2:45
Well, the early ones are genuinely impactful,
2:47
yes, and often quite surprising in just how precise
2:50
they can be. In orthopedics, The diagnostic capabilities
2:54
powered by AI are really quite striking. Imagine
2:57
AI algorithms, especially the deep learning ones,
3:00
assessing conditions like osteoarthritis from
3:01
x -rays with an aptitude that's comparable to,
3:04
or sometimes even better than, a doctor with
3:06
a decade's experience. That means faster, more
3:09
consistent diagnoses for patients. Beyond that,
3:12
we're seeing real strides in predicting patient
3:14
outcomes. Identify who is at high risk for complications
3:17
like parapathetic joint infection, or even predicting
3:21
which patients might feel dissatisfied after
3:24
their surgery. This allows for proactive intervention,
3:26
doesn't it? Not just reacting after something
3:28
goes wrong. And in sports, while that shift towards
3:30
proactive injury prevention is frankly a game
3:33
changer, AI acts like an early warning system.
3:35
It analyzes these complex multidimensional data
3:38
sets. wearables, biomechanics to predict muscle
3:41
injuries, manage athlete workloads. You mentioned
3:44
Health First earlier, they've deployed what,
3:45
17 predictive models using AI? Generated nearly
3:48
a thousand custom machine learning features to
3:50
improve health outcomes. That really shows its
3:53
power. That is remarkable. So, OK, if AI holds
3:56
all this promise, what's the single biggest hurdle?
3:59
What's stopping its widespread adoption and,
4:01
you know, realizing its full potential in this
4:03
field today? Without a doubt, it really boils
4:06
down to data, specifically its quality, its diversity
4:09
and crucially, its accessibility. Look, AI models
4:13
are only as good, only as robust as the data
4:15
they're trained on. That's fundamental. And the
4:18
reality is much of our health care data is still
4:21
Well, it's uncaptured or unstructured. It's varied,
4:24
stuck in different silos across different systems.
4:27
This makes it incredibly expensive and time -consuming
4:29
to clean it up, standardize it, integrate it
4:31
into formats that AI can actually use. And often,
4:34
we lack that really comprehensive data, don't
4:36
we? Especially the contextual stuff, social factors,
4:38
lifestyle choices, daily activities, all vital
4:41
for accurate prediction, but often missing. On
4:43
top of that, you've got the sensitivity of patient
4:45
data. It's highly regulated and rightly so for
4:48
privacy, but that does create significant barriers
4:50
for AI model training and deployment. That's
4:53
why initiatives to standardize data collection
4:55
and ensure secure ethical access are just so
4:58
critical. That paints a very clear picture of
5:00
that foundational challenge. Okay, now let's
5:02
unpack this core concept a bit more. You mentioned
5:05
augmented intelligence rather than just... artificial.
5:09
What does that actually mean for how clinicians
5:11
and sports scientists will work day to day? It
5:13
means a pretty profound transformation in how
5:15
we interact with technology, I think. At its
5:18
heart, artificial intelligence, AI, it refers
5:20
to computational techniques enabling machines
5:23
to do tasks that usually need human intelligence.
5:26
Things like learning, reasoning, solving problems,
5:29
spotting patterns, and crucially, the ability
5:32
to get better iteratively as they get more data.
5:34
Within AI, we've got these specific powerful
5:37
tools. There's machine learning, or ML, where
5:39
algorithms learn from data without being explicitly
5:41
programmed for every single scenario. Then deep
5:44
learning, DL, which uses these layered neural
5:46
networks to process huge amounts of complex data.
5:49
It's particularly good at image and pattern recognition.
5:52
And natural language processing, NLP, which helps
5:55
computers understand, interpret, and even generate
5:57
human language. Now the augmented intelligence
6:00
perspective, which bodies like the American College
6:02
of Physicians strongly advocate, views AI not
6:05
as a replacement for human expertise, no, but
6:07
as a powerful enhancement, a tool, and the evidence
6:10
consistently backs this up. When physicians work
6:12
with AI like reviewing mammograms for breast
6:14
cancer, the diagnostic outcomes are demonstrably
6:17
better compared to either working alone. It's
6:19
about leveraging AI's knack for processing vast
6:22
information, spotting subtle patterns, allowing
6:25
the clinician to focus their highly trained judgment
6:27
where it truly matters. It should lead to a more
6:29
efficient, more precise healthcare journey overall.
6:32
That's fascinating. So it's really about collaboration,
6:34
isn't it? Where the human brain and the machine's
6:37
processing power kind of create a sum greater
6:40
than their parts. Can you give us some specific,
6:43
tangible examples of how this augmentation plays
6:46
out in a clinic or a sports science setting?
6:50
Where's it making a real difference right now?
6:52
Absolutely. Let's think about something as fundamental
6:54
as clinical documentation. It takes up so much
6:57
time. Digital scribes, like Dax Copilot from
7:00
Microsoft, they're AI -powered, voice -enabled
7:03
solutions. They basically listen in during patient
7:06
interactions and automatically generate comprehensive
7:09
clinical notes. Now, this isn't just about saving
7:11
time, though that's significant. Studies show,
7:13
I think it was 70 % of physicians using it reported
7:16
better work -life balance, less burnout, and
7:18
perhaps even more importantly, 93 % of patients
7:20
felt their doctor was more personable, more engaged,
7:23
because they weren't constantly looking at a
7:24
screen and typing. It frees clinicians up to
7:26
actually connect with their patients rather than
7:29
wrestling with the admin side of things. That's
7:31
a huge impact on the human side of medicine.
7:33
Exactly. And we see this powerful augmentation
7:35
in diagnostic fields, too. Radiology, pathology,
7:38
ophthalmology, dermatology. Here, AI systems
7:42
can analyze images, identify anomalies, or subtle
7:44
patterns the human eye might miss, or maybe just
7:47
take much longer to spot. Again, it's not saying
7:49
the human is redundant. Far from it. The AI provides
7:52
a powerful second opinion maybe, or a rapid first
7:54
pass, flagging up areas of concern for the human
7:57
expert's focused review. Even seemingly mundane
8:00
admin tasks, patient scheduling, resource optimization
8:03
in a hospital, they're benefiting from AI too,
8:06
making things run smoother, more efficiently.
8:08
There's a quote from Dominique Rothenflue, a
8:10
highly respected orthopedic surgeon at Oxford
8:13
University Hospitals. He read it quite profoundly.
8:16
Machines may not necessarily replace physicians,
8:19
but physicians making use of AI will at some
8:21
point replace those not using it. And the sale
8:23
of the other. Now, that's not meant as a threat.
8:25
It's more of an imperative, really, for professionals
8:27
to adapt, to integrate these tools into their
8:29
practice, to ensure they stay at the forefront
8:31
and keep delivering the highest standard of care.
8:33
That is a powerful statement about the future
8:35
of practice. Okay, so AI is clearly reshaping
8:39
the before picture diagnosis planning. Let's
8:41
delve into that a bit more. Before a patient
8:43
even gets near an operating theater or an athlete
8:46
steps onto the pitch, AI is already at work.
8:48
Can you walk us through some of these really
8:50
critical preoperative applications? Yes, the
8:53
preoperative phase is, er, tremendously fertile
8:56
ground for AI. It's transforming how we diagnose,
8:59
predict outcomes, plan interventions. Let's start
9:02
with imaging and diagnosis. Traditionally, diagnosing
9:05
something like osteoarthritis, OA, from x -rays,
9:08
has been manual, often subjective, depends who's
9:10
looking, right? Now, AI algorithms, especially
9:13
these convolutional neural networks, CNNs, they're
9:15
basically AI systems really good at seeing and
9:18
interpreting patterns in images, a bit like our
9:21
own brain's process vision, they're automating
9:23
this. For instance, one significant study developed
9:26
a CNN model for hip OA diagnosis using hundreds
9:29
of radiographs. And what they found was compelling.
9:31
This AI model showed diagnostic aptitude comparable
9:34
to a physician with 10 years clinical experience.
9:37
10 years? Wow. Exactly. So what that means for
9:41
you, the patient, is potentially faster, more
9:43
consistent, highly accurate diagnoses. Similarly,
9:47
AI can accurately classify the severity of knee
9:50
OA using the standard Kellgren -Lorentz, or KL,
9:53
greeting system. It offers better reproducibility
9:56
than manual evaluation. Some newer approaches,
10:00
deep Siamese CNNs, can even give probability
10:03
distributions for ambiguous cases. That's incredibly
10:06
useful for clinical decisions, helping clinicians
10:08
gauge the AI's confidence level. But there's
10:11
a key challenge here, one we absolutely must
10:13
address, generalizability. Algorithms trained
10:17
mainly on data from one hospital might struggle
10:19
with different labeling systems or radiation
10:21
protocols elsewhere. Right, so it might work
10:23
brilliantly in one place, but not another. Precisely.
10:25
So for broad use across different hospitals,
10:27
different clinics, these models need really diverse,
10:30
extensive data sets for training. That's crucial.
10:32
That diverse data point keeps coming up, doesn't
10:34
it? Okay, so beyond just diagnosing what's happening
10:37
now, how is AI helping us predict what's likely
10:39
to happen for a patient's future? Yes, this is
10:42
where AI truly shines, I think. in its proactive
10:45
capability, moving us from just reacting to treatment
10:48
towards actually having some foresight. Machine
10:51
learning and deep learning models are now significantly
10:54
outperforming traditional statistical methods
10:56
in predicting, say, the structural progression
10:58
of OA, or identifying individuals at high risk
11:02
for needing a total joint replacement. a TJR.
11:05
For example, one study developed a deep learning
11:07
program using baseline knee x -rays to predict
11:10
medial joint space loss. That's a key sign of
11:12
OA getting worse. Their model achieved a significantly
11:16
higher AUC score. That's a metric for how well
11:18
a model distinguishes between groups, like those
11:21
who will progress versus those who won't. Their
11:23
AUC was, I think, 0 .799 compared to 0 .660 for
11:27
traditional models. That indicates a much better
11:30
predictive ability. Others found deep learning
11:32
could predict the need for total knee replacement,
11:34
TKA, just from radiographs, with pretty impressive
11:36
sensitivity and specificity. And that's not just
11:39
academic, is it? That has real -world implications.
11:41
Absolutely. This ability to predict lets us potentially
11:45
foresee osteoarthritis progression much earlier.
11:48
It enables proactive interventions that might
11:50
slow the disease down for a patient, or at least
11:53
help them prepare for what's likely coming. It's
11:55
about being prescriptive, not just descriptive,
11:58
giving people options earlier. So it's not just
12:01
about what's happening now, but what's likely
12:03
down the road. And how does that translate into
12:05
improving the patient experience? Especially
12:08
given, as you said, quite a few patients report
12:09
dissatisfaction even after a technically successful
12:12
surgery. Exactly. Patient satisfaction is a critical
12:15
challenge. It's quite sobering, really, that
12:17
20 to 30 percent of patients report no improvement
12:20
or even dissatisfaction after a total joint replacement.
12:23
So AI models are now being developed specifically
12:26
to predict this dissatisfaction. These models
12:28
can identify influential factors, things like
12:31
preoperative functional scores, maybe mental
12:33
health status allowing us to spot high -risk
12:36
patients beforehand. One study, for instance,
12:38
trained models that achieved strong AUCs, around
12:41
.81, in predicting knee -away dissatisfaction
12:44
two years after surgery. This means we can potentially
12:47
intervene with targeted preoperative education,
12:50
perhaps health optimization programs, or even
12:52
guide patients towards non -surgical options
12:54
earlier. That could reduce unnecessary treatments
12:57
and significant costs later on. It's about tailoring
13:00
the care pathway much more to the individual.
13:02
That sounds much more patient -centric. It aims
13:04
to be. And AI also excels at risk stratification
13:07
of various complications, making surgery safer
13:10
overall. For instance, predicting the need for
13:12
blood transfusion after TKA1 and ML model did
13:15
this with an excellent AUC of 0 .880 using just
13:19
six readily available preoperative factors. Or
13:22
for periprosthetic joint infection, PJI, which
13:25
is a really severe complication. An artificial
13:28
neural network identified obesity as a stronger
13:31
predictor than previously thought. And ML systems
13:33
have even outperformed traditional fixed criteria
13:35
for diagnosis, offering more adaptive, transparent
13:38
models, helping clinicians diagnose faster and
13:40
more accurately. Length of stay, LOS, that's
13:43
a major cost driver in hospitals. AI models can
13:46
identify key predictors age, BMI, operation time,
13:48
blood results. This allows for better resource
13:51
allocation, more efficient hospital management.
13:53
Better for everyone, really, but... And this
13:55
is really important, but a significant ethical
13:57
concern arises here. These powerful predictive
14:00
methods could inadvertently marginalize patients
14:02
flagged as too high risk. Ah yes, the risk of
14:04
creating a sort of uninsurable category but for
14:07
surgery. Potentially yes. We must be incredibly
14:10
vigilant to ensure these tools are applied equitably,
14:13
that no vulnerable individual is excluded from
14:15
necessary treatment just because of an algorithm's
14:18
prediction. The goal must always be to improve
14:21
access and outcomes for all. That's a really
14:23
crucial point about equitable access, ensuring
14:25
these powerful tools enhance rather than limit
14:28
patient care. Okay, moving beyond diagnostics
14:31
and risk, how is AI influencing these sort of
14:35
nuts and bolts of preparing for surgery? Things
14:37
like surgical planning and even implant design.
14:40
Yes, this is another area where AI is driving
14:42
some incredible innovation. AI software is now
14:45
directly informing implant design, positioning,
14:48
and 3D templating. All critical for successful
14:52
outcomes. For example, researchers have applied
14:54
machine learning to optimize short stem implant
14:57
design and hip replacements, aiming to reduce
14:59
something called stress shielding, which can
15:01
weaken the bone over time. Others have used deep
15:04
learning to estimate patient -specific adjustments
15:06
for optimal landmark use in total knee arthroplasty
15:09
planning, ensuring a much more precise fit for
15:11
that individual patient. There's also AI -based
15:14
3D preoperative planning software, like AI -HYPE
15:18
for total hip replacement, THA. Studies found
15:21
it offered significantly more accurate surgical
15:23
planning and component sizing compared to traditional
15:25
2D templating. And it saves surgeons valuable
15:28
time by giving these highly precise visualizations
15:30
of the anatomy. usually need CT scans, which
15:34
means more radiation and cost. That's often been
15:37
the case, yes, but future AI applications promise
15:39
to mitigate this. Some researchers, for instance,
15:42
have developed an innovative AI program that
15:44
can convert standard 2D X -rays into highly accurate
15:48
3D bone models. This could potentially reduce
15:50
the need for those costly higher radiation CT
15:53
scans, making this advanced planning much more
15:56
accessible. Another fascinating application is
15:59
automatic implant identification. With so many
16:02
THA procedures done now, correctly identifying
16:04
implant types for potential revisions is vital,
16:07
but it can be tricky and time consuming with
16:09
the sheer number of different implants out there.
16:11
So, researchers developed a deep learning tool
16:13
for automatic THA implant detection, trained
16:16
on over 240 ,000 radiographs. It achieved impressive
16:19
accuracy, 98 .9 % internal, 97 % external for
16:23
femoral components across different x -ray views.
16:26
And a unique, really vital aspect of their model
16:27
is it incorporates uncertainty quantification,
16:29
basically. It gauges its own confidence level
16:32
in outlier detection, flagging data points that
16:34
might skew results. These features are absolutely
16:36
vital for enhancing trust in the model and supporting
16:38
its potential for widespread clinical use. gives
16:40
surgeons confidence in what the AI is telling
16:43
them. OK, so AI is clearly reshaping the before
16:46
picture with remarkable precision. But what happens
16:49
during surgery? And then crucially, in that often
16:52
overlooked recovery phase, how is AI stepping
16:56
in there? That's an excellent question because
16:58
AI's utility definitely extends significantly
17:00
into the intraoperative and postoperative periods
17:03
too. During surgery, we're seeing this rapid
17:05
integration of AI into quite sophisticated tools.
17:09
Think augmented reality, AR -based navigation
17:11
systems, AI -assisted arthroscopic procedures,
17:15
and increasingly advanced robotic surgery. Robotic
17:18
surgery, already known for its minimally invasive
17:20
approach, brings greater precision. That can
17:22
lead to less blood loss, less pain for the patient,
17:25
and typically shorter recovery times. AI's role
17:28
here isn't just controlling the robot's arm.
17:30
It refines surgical planning by analyzing huge
17:32
amounts of data from previous successful surgeries.
17:35
And it provides real -time feedback during delicate
17:38
procedures like bone cutting or soft tissue resection.
17:41
This helps ensure remarkably consistent, reproducible
17:44
results, which is key for patient safety. getting
17:46
the best possible outcome. It sounds incredible,
17:48
but there must be challenges to adopting this
17:50
everywhere. Oh absolutely. As with any advanced
17:53
tech, there are hurdles. The financial investment
17:55
for robotic gear and the consumables is substantial.
17:58
System compatibility can be limited to specific
18:01
implant types, and crucially, extensive ongoing
18:05
training for surgeons and the whole theater staff
18:08
is absolutely essential for safe and effective
18:11
integration. It really requires a dedicated commitment
18:13
from healthcare systems. It's not just plug and
18:15
play. It sounds like incredible progress, but
18:17
the idea of AI assisting during surgery, it does
18:20
raise crucial questions, doesn't it? About human
18:22
expertise versus relying on the machine. How
18:25
do we ensure human intuition and skill remain
18:27
paramount? You've hit on a really critical point
18:30
there. A very valid concern is what we sometimes
18:32
call automation bias or over -reliance on AI.
18:36
If surgeons become overly dependent on the technology,
18:40
there's a genuine risk that their foundational
18:42
human intuition, their clinical judgment, even
18:45
their manual skills could diminish over time.
18:47
And if the technology fails or encounters an
18:50
unexpected situation the AI hasn't seen before,
18:53
that over -reliance could lead to severe consequences.
18:56
We can draw parallels perhaps with the airline
18:59
industry's use of autopilot. It's incredibly
19:01
efficient for routine flight, yes, but pilots
19:04
must remain highly skilled, capable of taking
19:07
manual control in emergencies. This also fundamentally
19:10
changes surgical training. It needs to evolve
19:12
to include not just manual skill, but also proficiency
19:15
in operating, interpreting, and critically understanding
19:18
these advanced machines. It's about building
19:19
a robust human AI team, not just delegating tasks
19:22
to a black box. That makes sense. Building a
19:25
team, not just relying on a tool. And what about
19:27
after the surgery, in the recovery phase? Right.
19:30
Postoperatively, AI is revolutionizing rehabilitation
19:32
and monitoring, primarily through remote patient
19:35
monitoring. The Internet of Things, IoT, and
19:38
mHealth mobile health solutions. They integrate
19:40
AI into wearable devices, sophisticated sensors,
19:43
smartphone apps, collecting vast amounts of real
19:46
-time patient data from their own home. Clinicians
19:49
can then remotely monitor a patient's rehab progress,
19:52
stepping in quickly if milestones aren't met
19:54
or if complications seem to be rising. For instance,
19:57
these innovations have shown real value in reducing
19:59
opioid use after surgery and improving adherence
20:02
to anticoagulation drugs, which are vital for
20:04
preventing serious blood clots. There was a study
20:07
developing motion -based machine learning software
20:09
for remote total knee arthroplasty monitoring.
20:12
It used a simple knee sleeve with Bluetooth sensors
20:15
transmitting data to a phone app. It gave clinicians
20:18
invaluable insights into the patient's mobility,
20:20
how well they were doing their exercises, and
20:22
could even flag up early signs of complications.
20:24
And crucially, patients found it really motivating,
20:27
engaging. It transforms how we oversee recovery
20:30
from just reactive clinic visits to a more proactive
20:33
continuous feedback loop directly from their
20:36
home. That sounds much more empowering for the
20:38
patient too. It really can be. And finally, AI
20:41
extends its powerful image analysis skills to
20:43
complication screening via imaging post -operatively
20:47
as well. It can detect early signs of implant
20:49
loosening or determine dislocation risk, often
20:52
with far greater accuracy and efficiency than
20:54
traditional methods. That allows for prompt intervention
20:57
before a problem really escalates. Prosthetic
21:00
loosening, for example, it's a leading cause
21:02
of arthroplasty failure, accounts for 30 % of
21:04
knee revisions, a huge 55 % of hip revisions.
21:07
It's a massive clinical and economic burden.
21:10
One group developed a fully automated CNN algorithm
21:12
to detect mechanical loosening of hip implants
21:15
from plain x -rays, reporting high sensitivity
21:17
around 91 .6%. This doesn't just supplement the
21:21
surgeon's decision -making, it also frees up
21:23
their valuable time for more patient -focused
21:25
activities. Similarly, hip dislocation after
21:27
replacement is a significant challenge, costs
21:29
a lot too. Another group developed a CNN, trained
21:32
on over 92 ,000 x -rays, to classify patients
21:35
for dislocation risk with about 89 % sensitivity.
21:38
Identifying low -risk patients with high confidence
21:40
could mean fewer post -operative restrictions,
21:43
fewer follow -up visits. That eases the burden
21:45
significantly on both patients and the healthcare
21:49
system. So, okay, broadening out from the clinical
21:52
setting. What does all this mean for athletes
21:54
from the weekend warrior maybe right up to the
21:58
elite professional? How is AI truly enabling
22:01
this shift from just reacting to injuries to
22:04
actively preventing them, transforming sports
22:06
medicine? Yes, this is a truly transformative
22:09
area. AI fundamentally shifts the whole paradigm
22:12
in sports medicine from reactive treatment to
22:15
proactive prevention. Musculoskeletal injuries
22:18
globally are a huge burden in sports, aren't
22:20
they, impacting careers, well -being. AI, mainly
22:24
through advanced machine learning and deep learning,
22:26
is designed to analyze these incredibly complex
22:28
multi -dimensional data sets. We're talking data
22:31
from high -tech wearables, detailed biomechanics
22:33
assessments, performance metrics, speed, power,
22:36
and even subtle psychological factors like sleep
22:38
quality or stress levels. This rich data lets
22:40
AI identify subtle patterns, risk factors that
22:43
traditional methods just relying on human observation
22:46
might easily miss. It's about getting ahead of
22:48
the curve, predicting vulnerability before an
22:50
injury actually occurs. So it's like seeing the
22:52
invisible signs. In a way, yes. In team sports,
22:56
AI models are already widely used to predict
22:58
muscle injuries, meticulously manage workloads
23:01
in demanding sports like football, basketball.
23:04
Think about high -contact sports rugby American
23:07
football. Here, AI precisely monitors collision
23:10
data to track an athlete's recovery from concussions,
23:13
providing early warnings based on cumulative
23:15
impacts over a season. Studies clearly highlight
23:18
AI's role as an early warning system, spotting
23:21
injury risks before they even manifest as a serious
23:23
problem. This proactive approach lets coaches,
23:26
medical staff, implement timely interventions,
23:28
adjust training loads, rest an athlete, potentially
23:30
saving their season, maybe even their career.
23:33
And what about individual sports? Where the demands
23:35
might be different? The application is equally
23:37
groundbreaking there. AI monitors real -time
23:40
biomechanical data, often from sensors embedded
23:43
right into clothing or equipment. It detects
23:45
subtle signs of fatigue or poor technique that
23:48
could predispose an athlete to injury. There
23:51
was the first prospective longitudinal cohort
23:53
study using machine learning to identify specific
23:56
risk factors for running -related injuries that's
23:59
revolutionizing prevention strategies for runners.
24:02
We're also seeing truly adaptive technologies.
24:05
Like mechatronic ski bindings using AI for real
24:08
-time knee protection in alpine skiing, they
24:10
instantly adjust based on forces to prevent injury.
24:12
That's incredible stuff. In sports like cycling,
24:15
swimming, tennis, gymnastics, AI analyses precise
24:18
biomechanics to prevent overuse injuries. CNN's
24:21
analyzing high dimensional wearable sensor data
24:23
can detect risky movements that would otherwise
24:25
just go completely unnoticed. And it helps with
24:27
getting back after an injury too. Absolutely.
24:30
AI plays a critical role in optimizing return
24:33
to play protocols. It doesn't just predict, it
24:35
prescribes. By creating personalized rehabilitation
24:38
plans based on an individual athlete's unique
24:40
data, AI acts as both a predictive and prescriptive
24:44
tool. It meticulously monitors recovery metrics,
24:47
tracks cumulative impacts, especially vital and
24:49
contact sports, to minimize the risk of re -injury
24:52
and support long -term athlete health. A prime
24:54
example in action is MAI Motion's AI applications.
24:57
They offer precise diagnostics, customized rehab
25:00
pathways, predictive analytics for athlete recovery,
25:02
and they've showed significant reductions in
25:04
recovery time, getting athletes back to peak
25:06
performance faster, safer. Similarly, Health
25:09
First, using closed -loop tech, deployed the
25:11
those 17 predictive models, nearly a thousand
25:14
customized machine learning features. It just
25:16
shows the immense value AI can drive by speeding
25:18
up insights into clinical workflows, improving
25:20
health outcomes across a population. It truly
25:23
heralds a new era of data -driven athlete recovery
25:25
and health management, making prevention not
25:27
just a goal, but a tangible reality. It's clearly
25:30
transformative. But as you've hinted, no technology
25:33
like this comes without challenges. While the
25:36
promise is clear, what are the significant hurdles
25:38
that really need addressing for AI to embed itself
25:41
seamlessly and responsibly into health care and
25:43
sports, especially thinking about those ethical
25:46
nuances? Indeed, there are significant hurdles.
25:49
They mainly cluster around data, ethical considerations,
25:52
and, well, operational realities. Firstly, data
25:55
quality diversity. As I mentioned earlier, AI's
25:59
effectiveness is entirely dependent on having
26:01
substantial, diverse, high -quality datasets.
26:04
The reality is, much healthcare data remains
26:06
uncaptured, unstructured, varied, stuck in silos.
26:10
It's hugely costly and time -consuming to clean,
26:13
standardize, integrate it for AI. Crucially,
26:16
comprehensive data, including those social determinants
26:18
of health, lifestyle choices, daily activities
26:20
vital for accurate prediction, are often systematically
26:23
lacking. Our own RU case survey found I think
26:26
76 % of the MSK community agrees it's difficult
26:29
to access and use patient data at scale, often
26:31
because of how data protection rules are interpreted.
26:34
For instance, researchers have been told to get
26:36
updated written consent from every single patient
26:38
for long -running datasets, even if generic consent
26:40
was given and data anonymized. That can render
26:42
vital research completely unviable. That sounds
26:44
like a major bottleneck. It really is. And it
26:47
leads directly into the complex ethical and legal
26:49
considerations. A major worry is algorithmic
26:52
bias. If AI systems are trained on data that
26:54
isn't representative or reflects historical biases,
26:57
they can inadvertently generate unequal outcomes
26:59
based on race, gender, ethnicity. This could,
27:02
unfortunately, lead to disparities in care quality,
27:04
accessibility for certain patient groups. There
27:07
are urgent calls for much greater transparency
27:09
in how these algorithms are built and robust
27:11
oversight to tackle this threat. Data privacy,
27:14
patient confidentiality are, of course, paramount.
27:17
Entering protected or identifiable patient info
27:20
into public AI systems, that's a clear violation
27:22
of ethical legal obligations. We need robust
27:25
ethical frameworks to guide responsible AI deployment,
27:28
always prioritizing patient dignity, autonomy,
27:30
privacy rights, Transparent, accessible, informed
27:33
consent processes are also vital, especially
27:35
for people with limited English proficiency,
27:37
ensuring patients fully understand how their
27:39
data will be used. That has to align with rules
27:41
like GDPR and NDP. The transparency aspect feels
27:44
particularly crucial doesn't it? If a doctor
27:47
can't actually explain how an AI reached a conclusion,
27:50
That surely arraigns trust. Absolutely. There's
27:53
a real risk of this automation bias or over -reliance
27:57
on AI we talked about, where over -dependence
28:00
could diminish human intuition, clinical skills.
28:02
If the tech fails or hits a unique situation
28:05
the AI wasn't trained for, that over -reliance
28:07
could have severe consequences. Clinicians need
28:10
to understand the underlying logic, not just
28:12
blindly accept if the model says so. This lack
28:15
of transparency, the black box problem, is a
28:17
significant barrier to trust and widespread adoption.
28:20
Clinicians must be able to critically evaluate
28:22
AI recommendations, just like any other diagnostic
28:25
test. And the operational side, beyond data and
28:28
ethics. Yes, several operational challenges too.
28:31
The inconsistent application of AI tech, due
28:34
to a lack of clear unified regulations, creates
28:37
uncertainty for developers and healthcare providers
28:39
alike. While AI can create new jobs, there's
28:42
also potential for job displacement as AI takes
28:45
on tasks humans used to do. That needs proactive
28:48
planning, retraining initiatives. There's a pressing
28:51
need for systemic oversight in AI research and
28:53
development generally to prevent exploitation,
28:56
ensure equitable outcomes for all patient demographics,
28:59
and fundamentally, even the most sophisticated
29:01
AI can't function without high -quality ground
29:04
truth data. That means accurate labeling, tagging
29:07
of orthopedic images by human experts is absolutely
29:10
fundamental for training reliable AI models.
29:13
That often requires significant human effort
29:15
and expertise right at the start. These are definitely
29:17
significant challenges, but you also mentioned
29:18
orthopedics seems uniquely positioned somehow
29:21
to overcome them. What makes this field such
29:24
fertile ground for AI and what are the key steps
29:26
needed to move forward and really unlock its
29:28
potential? Orthopedics is indeed uniquely fertile
29:31
ground for this kind of technological innovation.
29:34
It tends to have clear, well -defined pathways
29:36
for common diseases, which helps with data analysis,
29:39
and it's inherently a highly technical field,
29:42
always embracing innovation in devices, procedures,
29:45
As Professor Xavier Griffin from Queen Mary University
29:48
of London noted, orthopedics is always at the
29:51
vanguard when it comes to using large data sets
29:53
and embracing new tech. Yet, despite this natural
29:56
fit, it often gets overlooked in broader AI health
29:59
care discussions. A landmark NHSX report, for
30:01
instance, didn't even mention orthopedics. Cancer,
30:04
drug development often get the headlines, the
30:06
investment. And that, I believe, is a missed
30:08
opportunity because even small gains in orthopedics
30:11
can lead to better outcomes for huge patient
30:13
populations globally. just given the sheer volume
30:16
of musculoskeletal conditions. So how do we capitalize
30:19
on that potential? What's being done? Well, current
30:21
initiatives are crucial. Orthopedic Research
30:24
UK, ORUK, is playing a key role here, encouraging
30:27
AI application through education, strategic research
30:30
investment, pushing for data standardization,
30:33
fostering networking across the field, really
30:35
calling on the whole MSK community to get involved.
30:38
A shining example of data -driven success already
30:40
is the Getting it Right First Time program, GRLFT.
30:43
It leverages data sharing across orthopedic departments
30:47
to identify best practices, improve care, and
30:50
GRFT has led to remarkable results, reduced revision
30:53
rates, shorter hospital stays by a fifth, releasing
30:55
over 368 ,000 bed days, and has delivered staggering
30:59
NHS savings 696 million over just five years.
31:02
AI can further inform decision -making within
31:04
programs like GRFT by spotting those nuanced
31:07
patterns, helping remove existing biases in resource
31:10
allocation or patient care pathways. give us
31:13
some more concrete examples how AI is already
31:15
making a difference perhaps beyond the research
31:17
labs in everyday orthopedic practice patient
31:20
lives. Certainly. predictive modeling, for instance.
31:23
It's already identifying hip replacements at
31:25
risk of failure. And this isn't just stats. AI
31:28
is spotting subtle changes on x -rays missed
31:30
by the human eye, proving more accurate even
31:33
than experienced clinicians sometimes. This helps
31:36
flag problems early, preventing catastrophic
31:38
failures, yes, but also potentially saving the
31:40
NHS tens of millions by reducing the need for
31:43
routine screening of joints that are highly unlikely
31:45
to fail anyway. AI is also assisting with swift,
31:49
accurate classification of fractures. That's
31:51
vital given the significant backlog of unreported
31:54
x -rays in the UK, estimated over 300 ,000 waiting
31:56
more than 30 days. AI can help triage these,
32:00
ensure urgent ones get reviewed quickly, reducing
32:02
delays in care. That backlog figure is shocking.
32:05
It really highlights the need. Oro UK has even
32:08
taken an equity stake in radii devices, a promising
32:11
startup using AI and biomechanical modeling to
32:14
design better fitting prosthetic sockets for
32:17
amputees. The aim is to drastically reduce the
32:20
number of clinical visits needed for a comfortable
32:22
fit, potentially from up to nine visits down
32:24
to far fewer. That dramatically improves patient
32:27
comfort, speeds up their rehab. And then there's
32:29
the widespread use of wearables in patient apps,
32:32
things like Get You Better, Good Boost, the Versus
32:34
Arthritis AVA Chatbot. They harness AI to provide
32:37
personalized self -management support at home.
32:40
This reduces the need for GP appointments, physio
32:42
referrals, even medication by empowering patients
32:45
with tailored advice exercises. Our Oreo case
32:48
survey showed an overwhelming 91 % of the MSK
32:51
community agrees passive continuous monitoring
32:54
can significantly aid post -treatment recovery,
32:57
underscores the value of these AI tools. Those
32:59
are really impressive practical applications
33:01
showing direct benefits. So looking ahead now,
33:04
what are the key ingredients needed to accelerate
33:06
AI's integration, maximize its impact across
33:08
orthopedics, sports medicine? Several crucial
33:11
elements are needed for us to really unlock AI's
33:14
full potential, I think. Firstly, Continued enhancements
33:17
in machine learning and natural language processing.
33:19
Advancements in ML algorithms mean faster, more
33:22
accurate diagnoses from complex medical images,
33:25
X -rays, MRIs, CTs. NLP will streamline data
33:29
access, translate vast amounts of unstructured
33:31
medical jargon into actionable insights, significantly
33:34
improve patient management through speech recognition,
33:36
efficient record processing, huge operational
33:39
efficiencies there. Secondly, interdisciplinary
33:42
collaboration. Absolutely crucial. We need seamless
33:45
cooperation between clinicians, data scientists,
33:47
engineers, policymakers, and importantly, patients
33:50
themselves. As Dr. Justin Green in Orthopedic
33:53
Registrar rightly said, we need to involve genuine
33:55
data scientists. They bring skills clinicians
33:58
no matter how tech savvy cannot replicate. This
34:00
collaboration ensures AI solutions are both technically
34:03
sound and clinically relevant. Makes sense. You
34:05
need both sides of the coin. Exactly. Thirdly,
34:08
there's an urgent need for AI education. Our
34:10
ORR UK survey highlighted that startling figure,
34:13
75 % of MSK professionals feel they lack the
34:16
knowledge to apply AI and big data effectively.
34:19
We need structured, comprehensive training tailored
34:21
to MSK professionals. ORR UK is actively working
34:24
on developing such a program to upskill the workforce.
34:27
Fourth, research investment. critically lacking.
34:31
71 % of the MSK community agrees there's insufficient
34:33
funding for AI research in MSK health. We must
34:36
strategically fund innovative projects using
34:38
AML to reduce MSK pain, improve function, cut
34:41
costs, and fundamentally enhance quality of life
34:43
for millions. Fifth, data standardization. Paramount,
34:47
championing better data standards for MSK research,
34:50
including improved reporting of AI studies, addressing
34:52
eligibility criteria, ground truth data, data
34:54
set diversity model details, the need for rigorous
34:56
external validation, larger sample sizes. This
34:59
will help counter biases from narrow data sources,
35:01
making AI models more robust, more equitable.
35:04
So better data, better reporting. Precisely.
35:07
And finally, we need proactive regulatory frameworks,
35:10
ones that strike that crucial balance between
35:12
fostering innovation and ensuring patient protection.
35:15
They must ensure AI advancements contribute positively
35:18
to public health without infringing on individual
35:20
rights, and crucially, longitudinal studies and
35:23
validation across different sports environments,
35:25
diverse athlete groups. That's absolutely essential
35:28
to improve accuracy, generalizability, and finally
35:31
bring these groundbreaking innovations from promising
35:33
research into widespread, impactful clinical
35:36
practice. Right, now for our lightning round.
35:39
Quick, sharp insights. What's one AI technology
35:41
you believe is poised to make the biggest immediate
35:44
impact in orthopedics in, say, the next 12 to
35:47
18 months? I'd have to say AI -powered diagnostic
35:51
imaging for faster, more accurate detection,
35:54
coupled with predictive analytics for early intervention,
35:57
particularly around complications or predicting
35:59
outcomes. That's where the immediate wins are.
36:01
Okay. And for an aspiring athlete, maybe even
36:03
a weekend warrior like me, looking to leverage
36:06
AI for personal injury prevention, what's one
36:09
practical, actionable step they could take today?
36:11
I'd say start by embracing AI -powered wearable
36:14
technology if it's accessible. Use devices that
36:17
monitor your real -time biomechanics, your workload.
36:20
Look for apps or platforms offering personalized
36:23
feedback based on that data. Companies like MAI
36:26
Motion show how using this data for customized
36:28
rehab, predictive analytics can really cut recovery
36:32
time and prevent injuries. It gives you a powerful
36:34
edge. Good advice. Finally, for any healthcare
36:37
professional or sports scientist out there feeling,
36:39
well, maybe a bit overwhelmed by the sheer pace
36:42
of AI change, what single piece of advice would
36:44
you offer them? I'd say Embrace AI as augmented
36:48
intelligence, not as a replacement. Focus on
36:50
fostering that interdisciplinary collaboration
36:52
with data scientists and commit to continuous
36:55
learning, especially understanding AI ethics
36:57
and how to critically interpret AI -generated
36:59
data. Your human expertise remains invaluable.
37:02
AI just makes it potentially more powerful. OK,
37:04
brilliant. So let's try and crystallize our key
37:07
takeaways from today's Deal Dive. Firstly, artificial
37:11
intelligence is fundamentally an augmentative
37:13
tool. It's designed to... enhance not replace
37:16
the expertise of clinicians and sports scientists
37:19
leading to greater precision, greater efficiency
37:22
in care. Secondly, AI's applications are vast.
37:26
They span the entire patient journey, the athlete
37:29
life cycle from predictive diagnostics, personalized
37:31
treatment planning, right through to real -time
37:33
monitoring and advanced rehab. Thirdly, while
37:36
the benefits are immense, successful integration
37:38
really hinges on diligently tackling those crucial
37:41
challenges, ensuring access to high -quality,
37:43
diverse data, navigating complex ethical and
37:46
privacy concerns, and establishing clear, transparent
37:49
regulatory frameworks. Fourthly, orthopedics
37:52
in particular stands out as uniquely fertile
37:54
ground for AI innovation. It offers immense potential
37:57
for significant cost savings and vastly improved
37:59
outcomes for huge patient populations, provided
38:01
there's continued strategic investment and targeted
38:03
education. And finally, the future of AI in medicine
38:06
and sports demands unwavering interdisciplinary
38:08
collaboration and a steadfast commitment to integrating
38:11
this technology ethically, responsibly, ensuring
38:14
it always complements and elevates human expertise.
38:17
If you found this deep dive valuable today and
38:19
gained some new insights, please do consider
38:21
rating and sharing it with a colleague or a friend
38:23
who might also benefit from these discussions
38:25
on AI's impact in healthcare and sports. Thank
38:28
you so much for illuminating this incredibly
38:30
dynamic field for us today. It's been truly insightful.
38:33
And thank you, our listener, for joining us on
38:35
the deep dive. Do subscribe to make sure you
38:37
don't miss our next exploration into the fascinating
38:39
world of knowledge and insight. Now, as AI becomes
38:42
ever more intertwined with our health, just think.
38:45
How might our understanding of human performance,
38:47
of recovery, evolve when every subtle change,
38:50
every nuanced response can be quantified, understood
38:53
by algorithms? What new frontiers will this precise
38:56
understanding unlock for human potential?