Latest / Deep Dive Ortho — Orthopaedic Surgery, AI & Digital Health Podcast. / AI in planned Orthopaedic Care

Transcript

AI in planned Orthopaedic Care · Deep Dive Ortho — Orthopaedic Surgery, AI & Digital Health Podcast.

• 0:00
  1 .71 billion people. It's quite a number, isn't
• 0:04
  it? That's the estimate, worldwide, for people
• 0:06
  affected by musculoskeletal conditions. It really
• 0:09
  is, and it puts this, well, immense strain on
• 0:13
  healthcare systems everywhere, leads to those
• 0:16
  long waiting lists we hear about for planned
• 0:18
  procedures, and, you know, ultimately hits patient
• 0:21
  quality of life. It's a massive challenge for
• 0:24
  orthopedics, no question. A huge challenge. Welcome
• 0:27
  to the Deep Dive. This is where we take a stack
• 0:29
  of research, articles, reports, basically, whatever
• 0:32
  sources you bring to us, and we try to distill
• 0:34
  the really important knowledge, maybe some surprising
• 0:37
  insights, too. Cut through the noise. Exactly.
• 0:39
  Get you the essential takeaways. And today we
• 0:42
  are doing a deep dive into how digital technology,
• 0:46
  and specifically artificial intelligence, are
• 0:48
  looking to transform orthopedic care. We're focusing
• 0:51
  on planned procedures. Yes, a really fast -moving
• 0:54
  area. And guiding us through the material we've
• 0:56
  pulled together for this particular dive is our
• 0:58
  expert, someone who's brilliant at synthesizing
• 1:00
  these sometimes quite complex topics and helping
• 1:03
  us understand what really matters. Welcome. Thank
• 1:06
  you. It's great to jump into these sources. We've
• 1:09
  looked at quite a mix, actually, some detailed
• 1:11
  research papers on specific AI uses, broader
• 1:14
  reviews on digitalization's impact, and even
• 1:17
  some thinking from, well, outside healthcare
• 1:20
  on things like data and user engagement. Yeah,
• 1:23
  it's a rich set of sources, gives us different
• 1:25
  perspectives. So our mission for this deep dive
• 1:27
  really understand how AI and these other digital
• 1:30
  tools are actually influencing planned orthopedic
• 1:33
  care, starting right from assessment and planning
• 1:36
  all the way through the surgery itself and into
• 1:38
  recovery. The whole pathway. The whole pathway.
• 1:41
  So let's kick off with a quick rapid fire round
• 1:43
  just to sort of get our bearings. First question
• 1:47
  for you. Looking at these sources, what's the
• 1:50
  single biggest problem in orthopedics right now
• 1:52
  that digital tech is really trying to solve?
• 1:54
  Well, the fundamental issue, it seems, that keeps
• 1:56
  coming up is just managing the sheer volume of
• 2:00
  patients and, consequently, easing the burden
• 2:03
  on healthcare systems. Just the numbers. Exactly.
• 2:06
  Musculoskeletal conditions are a leading cause
• 2:08
  of disability globally. Demands rising, especially
• 2:11
  with aging populations. So... digital tools offer
• 2:15
  potential ways to handle that scale more efficiently.
• 2:18
  Okay, that makes sense. Second question, where
• 2:20
  are we seeing the most practical sort of in -use
• 2:23
  promise from AI in orthopedic practice today?
• 2:26
  What's actually happening now? Right now I'd
• 2:29
  say the most concrete applications seem to be
• 2:31
  in analyzing imaging, helping process x -rays,
• 2:35
  that kind of thing, and definitely assisting
• 2:38
  with preoperative planning and also predicting
• 2:41
  patient outcomes. Okay. That's where we are seeing
• 2:43
  some validated tools starting to emerge. Got
• 2:45
  it. And third question, what's the biggest hurdle,
• 2:48
  you know, the main sticking point preventing
• 2:50
  widespread adoption of these technologies across
• 2:52
  orthopedics? Across the board, a really critical
• 2:54
  challenge is data integration. Just getting different
• 2:57
  systems different types of patient information
• 2:59
  to talk to each other effective right the plumbing
• 3:02
  pretty much and linked very closely to that is
• 3:04
  the need for really robust validation of the
• 3:08
  tools themselves building trust both clinician
• 3:11
  and patient trust and crucially Navigating the
• 3:14
  pretty complex ethical landscape around AI and
• 3:17
  health care that frames it perfectly challenge
• 3:20
  and opportunity Okay, let's really unpack this.
• 3:23
  Let's start at the beginning of that patient
• 3:25
  journey Diagnosis and planning. You mentioned
• 3:28
  the massive patient volume. Can AI actually help
• 3:31
  right at that initial assessment stage? Well,
• 3:34
  that's certainly the hope and it's an area where
• 3:35
  research is very active. We are seeing applications
• 3:38
  of AI machine learning, deep learning being used
• 3:40
  to analyze orthopedic images like x -rays. Some
• 3:43
  studies mentioned in the sources suggest AI models
• 3:46
  can perform well. Comparably to or sometimes
• 3:48
  in specific tasks maybe even outperform clinicians
• 3:52
  or radiologists really outperform in what sort
• 3:54
  of tasks things like detecting certain types
• 3:56
  of fractures or identifying and staging how Severe
• 3:59
  osteoarthritis is on a standard 2d x -ray deep
• 4:03
  learning DL. It's a type of machine learning.
• 4:05
  It's particularly good with large amounts of
• 4:07
  image data That sounds like it could really speed
• 4:09
  things up at the front end. Are there specific
• 4:11
  examples in the sources of this kind of AI analysis
• 4:14
  being used? Yes, yeah. One source mentions a
• 4:18
  deep neural network, a DNN. which is just another
• 4:22
  term for a deep learning model that Lim and Colley's
• 4:24
  developed specifically for osteoarthritis screening.
• 4:28
  Okay. Another study by Ratzlaff used web -based
• 4:31
  questionnaires combined with some AI analysis
• 4:33
  for initial OA detection. The idea is tools that
• 4:37
  could potentially screen patients earlier or
• 4:39
  maybe help generate a list of possible diagnoses
• 4:42
  to investigate. Okay, but is that... Is that
• 4:45
  really ready for prime time, though? Can AI actually
• 4:48
  diagnose someone just from, say, an image or
• 4:51
  a questionnaire remotely? Well, this is where
• 4:53
  the sources give us a bit of a reality check.
• 4:56
  Right. Because while these screening tools might
• 4:58
  show high sensitivity, they're good at flagging
• 5:01
  potential issues, so they don't miss things.
• 5:02
  So they catch a lot. They catch a lot, yeah.
• 5:04
  But their specificity can be quite low. For instance,
• 5:07
  that JamR review highlights a Neoway web questionnaire
• 5:09
  with 73 % sensitivity. It sounds good. But it
• 5:13
  also notes symptom checker specificity could
• 5:16
  be as low as, say, 23 % to 27 % in one study
• 5:20
  they looked at. Ah, OK. So high sensitivity,
• 5:23
  low specificity means? You get a lot of false
• 5:25
  positives. You flag lots of people who don't
• 5:27
  actually have the condition. Right. So it might
• 5:28
  send perfectly healthy people down the pathway
• 5:30
  for more checks. Precisely. And that JMI review
• 5:34
  is pretty clear. Despite these developments,
• 5:37
  there's actually limited evidence that current
• 5:39
  standalone digital health apps can reliably establish
• 5:43
  a primary diagnosis remotely, especially when
• 5:47
  you compare them to traditional clinical methods.
• 5:49
  Like seeing a doctor face -to -face or having
• 5:51
  a specialist review detailed scans. Exactly.
• 5:54
  And why is that limitation so significant? What
• 5:56
  are these digital tools missing compared to,
• 5:59
  say, a clinic visit? Well, they often lack the
• 6:02
  integration of crucial clinical data. A doctor
• 6:05
  gathers information from a physical exam, right?
• 6:08
  They interpret imaging, but in the full context
• 6:10
  of the patient's history, maybe order lab tests.
• 6:12
  The whole picture. The whole picture. Current
• 6:15
  remote apps often operate in silos, maybe just
• 6:17
  using one or two data streams. Plus, as the Psycho2J
• 6:21
  review points out, there's often a mismatch between
• 6:23
  how bad arthritis looks on an x -ray and the
• 6:26
  patient's actual symptoms. Oh, right. You can
• 6:29
  have a bad x -ray but feel OK or vice versa.
• 6:32
  Exactly. So an AI relying only on image severity
• 6:34
  could easily miss or misinterpret that clinical
• 6:37
  nuance. So OK. While AI offers, let's say, tantalizing
• 6:41
  potential for initial sifting or providing some
• 6:44
  info, it's definitely not replacing the full
• 6:46
  diagnostic process yet. It needs more data input,
• 6:49
  more context. Precisely. Based on these sources,
• 6:52
  the current clinical utility for standalone remote
• 6:54
  diagnosis seems quite limited. Okay, that's clear.
• 6:57
  Let's move a bit further into those early stages
• 6:59
  then, specifically preoperative planning. How's
• 7:02
  AI being used there to help surgeons get ready
• 7:04
  for a procedure? Now this is an area showing
• 7:06
  I think stronger practical application today.
• 7:09
  AI is being integrated into software that really
• 7:11
  helps with surgical preparation. Things like
• 7:13
  helping optimize implant design. There's mention
• 7:16
  of a machine learning application for hip stem
• 7:19
  design, for instance, or facilitating patient
• 7:22
  -specific adjustments, creating really detailed
• 7:24
  3D templates based on the patient's imaging data.
• 7:27
  So tailoring the plan to the individual. Exactly.
• 7:31
  One study used deep learning to identify the
• 7:33
  best anatomical landmarks for a total knee replacement,
• 7:37
  helping surgeons plan their cuts and implant
• 7:38
  placement more accurately. And we're also seeing
• 7:41
  AI used on post -operative images to predict
• 7:43
  future risks. There's a fascinating example of
• 7:46
  the sources about using a convolutional neural
• 7:48
  network, a CNN. which is great for image analysis.
• 7:52
  That achieved 89 % sensitivity in classifying
• 7:55
  total hip replacement patients for their risk
• 7:57
  of dislocation just based on a single post -op
• 8:00
  x -ray. Wow, 89%. That's very specific. What's
• 8:03
  the actual practical value of predicting dislocation
• 8:06
  risk like that from an x -ray? Well, if you can
• 8:08
  reliably identify patients predicted to be low
• 8:10
  risk, you could potentially tailor their post
• 8:13
  -operative protocols. So not everyone needs the
• 8:15
  same strict rules afterwards. Potentially, yes.
• 8:19
  Instead of a one -size -fits -all approach, maybe
• 8:21
  lower -risk patients could have fewer movement
• 8:23
  restrictions. That might lead to faster or perhaps
• 8:26
  less burdensome recoveries, while of course making
• 8:29
  sure the high -risk patients get all the necessary
• 8:31
  precautions. Right. It's about personalizing
• 8:34
  that post -op care using data to assess risk.
• 8:37
  Exactly. Data -informed risk assessment. So from
• 8:41
  refining the actual implants to patient -specific
• 8:44
  3D planning and even predicting post -op risks
• 8:48
  during planning, AI seems to be really pushing
• 8:50
  towards making surgical strategies more precise
• 8:53
  and personalized. I think that's a fair summary,
• 8:55
  yes. OK, let's shift gears a bit now. Moving
• 8:57
  beyond the planning phase into the actual treatment
• 8:59
  and then the post -operative journey, what role
• 9:02
  are AI and other digital tools playing there?
• 9:05
  Well, this is where we see AI really heavily
• 9:07
  involved in predictive analytics. predicting
• 9:09
  patient outcomes, predicting risks. The sources
• 9:11
  detail quite a few models being developed and
• 9:13
  tested for specific complications or maybe resource
• 9:16
  needs. Like what kind of things? For instance,
• 9:19
  predicting the need for blood transfusions after
• 9:21
  a total knee replacement. That's discussed with
• 9:24
  one machine learning model achieving an AUC of
• 9:27
  0 .88. AUC area under the curve. That's a measure
• 9:30
  of accuracy, right? Yes. Essentially how well
• 9:33
  the model distinguishes between groups. In this
• 9:36
  case, patients who will need a transfusion. versus
• 9:39
  those who won't. 0 .88 is considered pretty good
• 9:42
  performance. And these predictive models, they're
• 9:44
  also being used for sort of operational efficiency
• 9:48
  too, like predicting how long someone might stay
• 9:51
  in hospital or if they might be readmitted. Oh,
• 9:53
  absolutely. Predicting length of stay after procedures
• 9:56
  like total hip replacement is a key area. Same
• 9:58
  for predicting unplanned readmissions. Right.
• 10:01
  And these predictions aren't just useful clinically.
• 10:03
  The BOA source specifically highlights how predictive
• 10:06
  analytics can really help improve hospital efficiency
• 10:09
  and resource management. Forecasting demand,
• 10:13
  managing patient flow better. Makes sense. They
• 10:14
  even mentioned studies showing high accuracy
• 10:17
  for predicting things like how long a specific
• 10:20
  surgical case might take. And this predictive
• 10:22
  capability also extends to quite serious complications.
• 10:26
  Things like predicting periprosthetic joint infection
• 10:29
  PJI or surgical site infections after knee replacements.
• 10:33
  That allows for more targeted prevention efforts
• 10:35
  in those identified as high risk. OK, predicting
• 10:39
  objective clinical outcomes like infection or
• 10:42
  length of stay, that makes intuitive sense. But
• 10:44
  some sources mentioned predicting patient satisfaction.
• 10:47
  That seems. Well, much harder to pin down and
• 10:50
  predict. It is definitely complex. And the sources
• 10:53
  acknowledge that even with technically successful
• 10:55
  surgery, a significant chunk of patients, maybe
• 10:58
  20%, sometimes even 30 % report they haven't
• 11:01
  really improved, or they're actually dissatisfied
• 11:03
  after a joint replacement. Wow, that high. It
• 11:05
  can be, yes. What's interesting is that machine
• 11:07
  learning models are being developed to try and
• 11:09
  predict this dissatisfaction. And often, factors
• 11:12
  like the patient's preoperative functional level
• 11:14
  and their mental health status emerge as really
• 11:17
  key influencers. So it's not just about the surgery
• 11:20
  itself? Not entirely, no. It highlights that
• 11:23
  satisfaction is multifaceted. One study mentioned
• 11:26
  used a multimodal model pulling in various data
• 11:29
  points and achieved an AUC of 0 .816 in predicting
• 11:33
  dissatisfaction. But thinking about the data
• 11:36
  used for these complex predictions, can we really
• 11:39
  rely heavily on things like patient reported
• 11:42
  outcome measures PROMs? Are they robust enough
• 11:45
  on their own? That's a really important point
• 11:47
  and the sources do caution against relying solely
• 11:50
  on PROMs for prediction. They can suffer from
• 11:52
  what's called a ceiling effect. Meaning? Meaning
• 11:54
  patients who already have relatively high function
• 11:56
  before surgery might not show a large measured
• 11:59
  improvement on a problem score afterwards, even
• 12:02
  if they personally feel the surgery was a huge
• 12:04
  success for them. Ah, I see. The scale doesn't
• 12:07
  capture their perceived benefit. Exactly. It
• 12:10
  points to needing richer, perhaps more nuanced
• 12:12
  data beyond just problem scores to get really
• 12:15
  accurate predictions, especially for something
• 12:16
  like satisfaction. And this whole area of predictive
• 12:19
  power, especially around risk, it brings up quite
• 12:22
  a significant ethical point that was raised in
• 12:24
  the sources, doesn't it? Yes, it absolutely does.
• 12:26
  It's a critical consideration. I mean, while
• 12:29
  risk stratification is helpful for tailoring
• 12:31
  care and allocating resources effectively, using
• 12:35
  predictive models to label patients based on
• 12:37
  their predicted outcomes or complication risk
• 12:40
  carries a real potential for marginalization.
• 12:42
  Yeah, so? Well, there's a genuine concern that
• 12:46
  patients who are deemed too high risk by an algorithm
• 12:49
  might be perhaps unfairly labeled as inoperable.
• 12:53
  even if they might still stand to benefit significantly
• 12:55
  from surgery. So the algorithm could effectively
• 12:58
  deny them care. That's the fear. It raises very
• 13:01
  challenging questions about equitable access
• 13:03
  to care and what role algorithm should play in
• 13:06
  fundamental clinical decisions. It's a complex
• 13:08
  ethical landscape we need to navigate very carefully
• 13:11
  as these tools get more sophisticated. Definitely
• 13:13
  something to watch. Okay, beyond the predictive
• 13:16
  AI side, what other digital tools are factoring
• 13:19
  into the treatment and monitoring phases? Well,
• 13:21
  robotic assisted surgery is obviously a key technology
• 13:24
  that's integrated into treatment itself. It's
• 13:27
  often highlighted for enhancing precision in
• 13:29
  certain procedures, particularly mentioned in
• 13:32
  spinal surgery examples in the sources. Right,
• 13:34
  the robots. And then increasingly remote monitoring
• 13:37
  tools are becoming important, especially postoperatively.
• 13:41
  So things like wearables, external sensors, and
• 13:44
  even the development of so -called smart implants.
• 13:47
  Smart implants? That sounds very futuristic.
• 13:50
  What sort of data are they actually providing?
• 13:52
  Well... They're becoming much more of a reality.
• 13:54
  The digitalization review we looked at mentions
• 13:57
  smart implants being explored in areas like total
• 13:59
  knee replacements or for monitoring how well
• 14:03
  spinal fusion is healing. The idea is they can
• 14:06
  provide real -time data on things like the biomechanical
• 14:08
  load actually being placed on the implant. Inside
• 14:11
  the body. Inside the body, yes. Or the status
• 14:13
  of bone healing around it, potentially even detecting
• 14:16
  early signs of the implant loosening or maybe
• 14:18
  the formation of infection biofilms. Really granular
• 14:21
  data. Fascinating. And what about the more familiar
• 14:24
  things like wearables or external sensors? Yeah,
• 14:27
  they're being used quite a bit to track patient
• 14:29
  activity levels, their gait patterns, range of
• 14:31
  motion during recovery. Wearables have actually
• 14:34
  been validated for accurately monitoring these
• 14:36
  sorts of parameters post -operatively for things
• 14:39
  like knee and shoulder surgeries. And the main
• 14:41
  benefit there is? The major benefit is enabling
• 14:44
  effective remote recovery monitoring. It allows
• 14:48
  clinicians to potentially tailor rehab plans
• 14:51
  based on real data and maybe identify complications
• 14:54
  earlier without needing so many frequent in -person
• 14:57
  appointments. Right. Convenience and potentially
• 14:59
  better oversight. That's the goal. OK. So all
• 15:02
  of this you've got. imaging data, clinical notes,
• 15:05
  sensor data from wearables, patient reports like
• 15:08
  PROMs, maybe even data streaming from a smart
• 15:11
  implant. That generates just an immense amount
• 15:13
  of information, doesn't it? How do orthopedic
• 15:15
  systems even begin to make sense of that, that
• 15:17
  kind of fire hose of data? That is precisely
• 15:20
  the major hurdle we touched on earlier. Data
• 15:22
  integration. It's huge. And as the Digital Twins'
• 15:26
  TED Talk highlighted, data from complex systems
• 15:28
  like a human body undergoing recovery is often,
• 15:32
  well, sparse in some ways, noisy in others, sometimes
• 15:36
  indirect. And it often only gives you a snapshot
• 15:39
  of now. It doesn't automatically tell you what's
• 15:41
  going to happen next or why something is happening.
• 15:44
  So just having more data isn't necessarily the
• 15:46
  answer on its own. Not necessarily no. Yeah.
• 15:49
  And this is where the concept of a digital twin
• 15:51
  comes in as a potential maybe future direction.
• 15:53
  A digital twin. Explain that a bit more. OK.
• 15:55
  So the idea is combining predictive models. These
• 15:58
  could be models based on physics. like how joints
• 16:00
  biomechanically move, or models learned from
• 16:03
  data using AI combining those models with something
• 16:06
  called data assimilation. Data assimilation.
• 16:08
  Yeah, it basically means continuously feeding
• 16:10
  that model new real -time data from the actual
• 16:13
  patient. So sensor data, maybe PROMs they submit,
• 16:17
  perhaps even aspects of their lifestyle tracked
• 16:19
  via an app. You feed that data in constantly.
• 16:21
  To keep the model updated. Exactly, to keep the
• 16:24
  digital model of that specific patient or maybe
• 16:28
  their implant dynamic and current and reflective
• 16:31
  of their reality. So you're building a constantly
• 16:33
  updating digital replica of the patient's orthopedic
• 16:37
  situation. That's a good way to put it, yes.
• 16:39
  Applying this concept in orthopedics could potentially...
• 16:43
  create a really comprehensive, integrated digital
• 16:46
  representation of the patient. It could give
• 16:48
  clinicians a much deeper, more dynamic understanding
• 16:51
  than static records usually allow. Leading to
• 16:54
  better predictions and more informed decisions,
• 16:56
  presumably. That's the hope, but... And it's
• 16:59
  a big but. Making this work requires establishing
• 17:02
  reliable, connected clinical databases, the whole
• 17:06
  big data infrastructure. And it needs pretty
• 17:09
  unprecedented collaboration across different
• 17:11
  disciplines. You need the clinicians, the data
• 17:13
  scientists, legal experts for privacy and security.
• 17:16
  And crucially, you need the patients involved,
• 17:17
  too. And that brings us right back to the patient,
• 17:19
  doesn't it? Many of these tools, especially the
• 17:22
  monitoring apps, the data collection devices,
• 17:25
  they require the patient to actively participate.
• 17:27
  And we know getting people to consistently use
• 17:30
  health care apps can be a real challenge. It
• 17:32
  really, really can be. maybe insights from completely
• 17:37
  different areas like the Duolingo TED Talk become
• 17:40
  relevant. The language learning app, how's that
• 17:42
  relevant? Well, the core idea discussed there
• 17:44
  is about applying psychological techniques that
• 17:47
  consumer tech uses very effectively. Things like
• 17:51
  gamification, smart notifications, visual progress
• 17:54
  trackers. Making it engaging. Exactly. Not to
• 17:57
  make healthcare addictive like social media,
• 17:59
  obviously, but to make... Using the necessary
• 18:01
  tools, tracking your rehab exercises, reporting
• 18:04
  your pain levels, allowing sensors to collect
• 18:07
  data to make doing those things consistently
• 18:09
  more engaging, maybe even rewarding. Sort of
• 18:12
  making the broccoli taste like dessert, as the
• 18:14
  speaker put it. Making the healthy, necessary
• 18:16
  thing feel a bit more appealing. Precisely that
• 18:19
  analogy. For orthopedics, it means thinking hard
• 18:22
  about designing apps or integrating wearables
• 18:25
  in a way that genuinely encourages patients to
• 18:27
  stick with it, to track their progress, report
• 18:30
  how they're feeling, allow the data collection.
• 18:33
  Because the best remote monitoring tech in the
• 18:35
  world is useless if the patient stops using it
• 18:38
  after a week. Patient buy -in and continued engagement
• 18:41
  is absolutely essential for the data flow these
• 18:44
  advanced systems need. Couldn't agree more. It's
• 18:46
  critical. So let's try and synthesize where we
• 18:48
  stand then, according to these sources. The potential
• 18:51
  for digital tech and AI in orthopedics looks,
• 18:54
  well, immense. It seems to cover the entire patient
• 18:57
  journey, right from that first contact all the
• 19:00
  way through recovery. I think that's right. There's
• 19:02
  clearly significant promise for improving efficiency,
• 19:05
  for personalizing treatment pathways, enhancing
• 19:07
  safety protocols, and potentially improving patient
• 19:10
  outcomes, especially facing that rising demand
• 19:12
  we talked about. But in it feels like a significant,
• 19:15
  but running through the sources, many of these
• 19:16
  applications are still very much in development
• 19:18
  or maybe in the early stages of validation. Yes,
• 19:21
  absolutely. Widespread routine adoption requires
• 19:25
  more robust clinical evidence for many applications.
• 19:28
  It needs standardized data protocols. We need
• 19:30
  to overcome those significant data integration
• 19:33
  challenges. We need to build trust and very importantly,
• 19:36
  navigate those ethical considerations carefully
• 19:38
  to ensure fair and equitable access and application.
• 19:42
  Lots of potential, lots of work still to do.
• 19:44
  Okay, fascinating insights. Let's finish off
• 19:46
  with our lightning round quick questions, sharp
• 19:48
  answers if you can. Based on these sources, what's
• 19:52
  the single piece of digital tech that orthopedic
• 19:55
  professionals should really be paying closest
• 19:56
  attention to in the next, say, one to two years?
• 20:00
  I'd say prediction models. Specifically, models
• 20:02
  that move beyond using just one type of data
• 20:04
  and start integrating clinical information, imaging
• 20:07
  data, and potentially sensor data together for
• 20:09
  more holistic prediction. Okay, integrated prediction
• 20:11
  models. Beyond the technology itself, what's
• 20:14
  the biggest non -technical challenge to successful
• 20:16
  adoption of all these tools? For me, it has to
• 20:19
  be establishing that robust, secure, and genuinely
• 20:23
  integrated data infrastructure across different
• 20:26
  parts of the healthcare system. It underpins
• 20:29
  Everything else. The data infrastructure. Got
• 20:31
  it. And finally, what's one small, maybe actionable
• 20:35
  step an orthopedic professional listening could
• 20:36
  take today based on what we've discussed? I think
• 20:39
  a useful step would be to actively explore the
• 20:42
  current state of data collection and accessibility
• 20:44
  within their own clinical environment. Just understand
• 20:47
  what data exists, how accessible it is, and identify
• 20:51
  potential opportunities for maybe better integration
• 20:53
  or better utilization of the data sources they
• 20:55
  already have. Start by understanding your own
• 20:57
  data landscape. Excellent. OK, let's quickly
• 21:01
  recap the key takeaways from this deep dive for
• 21:03
  you. Good idea. First, digital technology, especially
• 21:06
  AI, it isn't just a nice to have anymore. The
• 21:09
  sources really frame it as becoming essential
• 21:11
  if we're going to manage the growing global demand
• 21:13
  in orthopedics. Unavoidable, really. Second,
• 21:16
  AI is showing practical promise right across
• 21:18
  that patient journey, improving image analysis,
• 21:21
  helping plan surgeries more precisely, and getting
• 21:24
  better at predicting patient outcomes and risks.
• 21:26
  Yes, definite progress there. Third, while digital
• 21:29
  tools offer some screening potential, they currently
• 21:32
  have clear limitations for standalone remote
• 21:34
  diagnosis. That highlights the absolute need
• 21:37
  to integrate multiple data sources, the exam
• 21:40
  findings, labs, imaging, sensor data, not just
• 21:44
  rely on one isolate. piece. Crucial point, that
• 21:47
  integration. Fourth, making all this actually
• 21:50
  work in practice requires building robust, secure
• 21:53
  data infrastructure. And we have to tackle significant
• 21:56
  non -technical hurdles like data integration
• 21:58
  itself, validation, building trust, and ensuring
• 22:01
  equitable access for all patients. Big challenges,
• 22:04
  but necessary ones. And finally, for those tools
• 22:06
  that need active patient participation, the apps,
• 22:09
  the wearables, we really need to think about
• 22:11
  how to genuinely engage users. Maybe borrowing
• 22:14
  successful techniques from other digital fields
• 22:16
  is key if these tools are going to deliver their
• 22:18
  full value. Engagement is key, definitely. If
• 22:21
  you found this deep dive valuable, please do
• 22:23
  take a moment to rate and share it. It really
• 22:25
  helps other professionals like you discover these
• 22:27
  insights. Please do. And as you reflect on the
• 22:30
  future of orthopedic care and the increasing
• 22:33
  power of these digital tools, here's maybe something
• 22:35
  to ponder. If AI gets really good at predicting
• 22:38
  a patient's likelihood of satisfaction or their
• 22:41
  risk of specific complications, how does that
• 22:44
  fundamentally change the nature of the conversation,
• 22:47
  that shared decision -making process that happens
• 22:49
  between the doctor and the patient in the clinic
• 22:51
  room? What does that look like?