WEBVTT 00:00:00.000 --> 00:00:04.099 1 .71 billion people. It's quite a number, isn't 00:00:04.099 --> 00:00:06.500 it? That's the estimate, worldwide, for people 00:00:06.500 --> 00:00:09.939 affected by musculoskeletal conditions. It really 00:00:09.939 --> 00:00:13.060 is, and it puts this, well, immense strain on 00:00:13.060 --> 00:00:16.399 healthcare systems everywhere, leads to those 00:00:16.399 --> 00:00:18.420 long waiting lists we hear about for planned 00:00:18.420 --> 00:00:21.420 procedures, and, you know, ultimately hits patient 00:00:21.420 --> 00:00:24.239 quality of life. It's a massive challenge for 00:00:24.239 --> 00:00:27.579 orthopedics, no question. A huge challenge. Welcome 00:00:27.579 --> 00:00:29.829 to the Deep Dive. This is where we take a stack 00:00:29.829 --> 00:00:32.429 of research, articles, reports, basically, whatever 00:00:32.429 --> 00:00:34.750 sources you bring to us, and we try to distill 00:00:34.750 --> 00:00:37.049 the really important knowledge, maybe some surprising 00:00:37.049 --> 00:00:39.189 insights, too. Cut through the noise. Exactly. 00:00:39.409 --> 00:00:42.509 Get you the essential takeaways. And today we 00:00:42.509 --> 00:00:45.490 are doing a deep dive into how digital technology, 00:00:46.090 --> 00:00:48.409 and specifically artificial intelligence, are 00:00:48.409 --> 00:00:51.420 looking to transform orthopedic care. We're focusing 00:00:51.420 --> 00:00:54.020 on planned procedures. Yes, a really fast -moving 00:00:54.020 --> 00:00:56.240 area. And guiding us through the material we've 00:00:56.240 --> 00:00:58.259 pulled together for this particular dive is our 00:00:58.259 --> 00:01:00.679 expert, someone who's brilliant at synthesizing 00:01:00.679 --> 00:01:03.780 these sometimes quite complex topics and helping 00:01:03.780 --> 00:01:06.640 us understand what really matters. Welcome. Thank 00:01:06.640 --> 00:01:09.579 you. It's great to jump into these sources. We've 00:01:09.579 --> 00:01:11.219 looked at quite a mix, actually, some detailed 00:01:11.219 --> 00:01:14.659 research papers on specific AI uses, broader 00:01:14.659 --> 00:01:17.659 reviews on digitalization's impact, and even 00:01:17.659 --> 00:01:20.159 some thinking from, well, outside healthcare 00:01:20.159 --> 00:01:23.000 on things like data and user engagement. Yeah, 00:01:23.079 --> 00:01:25.000 it's a rich set of sources, gives us different 00:01:25.000 --> 00:01:27.519 perspectives. So our mission for this deep dive 00:01:27.519 --> 00:01:30.599 really understand how AI and these other digital 00:01:30.599 --> 00:01:33.659 tools are actually influencing planned orthopedic 00:01:33.659 --> 00:01:36.420 care, starting right from assessment and planning 00:01:36.420 --> 00:01:38.659 all the way through the surgery itself and into 00:01:38.659 --> 00:01:41.120 recovery. The whole pathway. The whole pathway. 00:01:41.420 --> 00:01:43.819 So let's kick off with a quick rapid fire round 00:01:43.819 --> 00:01:47.180 just to sort of get our bearings. First question 00:01:47.180 --> 00:01:50.030 for you. Looking at these sources, what's the 00:01:50.030 --> 00:01:52.329 single biggest problem in orthopedics right now 00:01:52.329 --> 00:01:54.129 that digital tech is really trying to solve? 00:01:54.409 --> 00:01:56.370 Well, the fundamental issue, it seems, that keeps 00:01:56.370 --> 00:02:00.370 coming up is just managing the sheer volume of 00:02:00.370 --> 00:02:03.590 patients and, consequently, easing the burden 00:02:03.590 --> 00:02:06.150 on healthcare systems. Just the numbers. Exactly. 00:02:06.750 --> 00:02:08.789 Musculoskeletal conditions are a leading cause 00:02:08.789 --> 00:02:11.590 of disability globally. Demands rising, especially 00:02:11.590 --> 00:02:15.240 with aging populations. So... digital tools offer 00:02:15.240 --> 00:02:17.639 potential ways to handle that scale more efficiently. 00:02:18.080 --> 00:02:20.879 Okay, that makes sense. Second question, where 00:02:20.879 --> 00:02:23.659 are we seeing the most practical sort of in -use 00:02:23.659 --> 00:02:26.740 promise from AI in orthopedic practice today? 00:02:26.879 --> 00:02:29.020 What's actually happening now? Right now I'd 00:02:29.020 --> 00:02:31.659 say the most concrete applications seem to be 00:02:31.659 --> 00:02:35.860 in analyzing imaging, helping process x -rays, 00:02:35.979 --> 00:02:38.300 that kind of thing, and definitely assisting 00:02:38.300 --> 00:02:41.020 with preoperative planning and also predicting 00:02:41.020 --> 00:02:43.379 patient outcomes. Okay. That's where we are seeing 00:02:43.379 --> 00:02:45.560 some validated tools starting to emerge. Got 00:02:45.560 --> 00:02:48.099 it. And third question, what's the biggest hurdle, 00:02:48.340 --> 00:02:50.199 you know, the main sticking point preventing 00:02:50.199 --> 00:02:52.539 widespread adoption of these technologies across 00:02:52.539 --> 00:02:54.919 orthopedics? Across the board, a really critical 00:02:54.919 --> 00:02:57.800 challenge is data integration. Just getting different 00:02:57.800 --> 00:02:59.840 systems different types of patient information 00:02:59.840 --> 00:03:02.180 to talk to each other effective right the plumbing 00:03:02.180 --> 00:03:04.879 pretty much and linked very closely to that is 00:03:04.879 --> 00:03:08.580 the need for really robust validation of the 00:03:08.580 --> 00:03:11.000 tools themselves building trust both clinician 00:03:11.000 --> 00:03:14.840 and patient trust and crucially Navigating the 00:03:14.840 --> 00:03:17.340 pretty complex ethical landscape around AI and 00:03:17.340 --> 00:03:20.340 health care that frames it perfectly challenge 00:03:20.340 --> 00:03:23.620 and opportunity Okay, let's really unpack this. 00:03:23.620 --> 00:03:25.639 Let's start at the beginning of that patient 00:03:25.639 --> 00:03:28.300 journey Diagnosis and planning. You mentioned 00:03:28.300 --> 00:03:31.479 the massive patient volume. Can AI actually help 00:03:31.479 --> 00:03:33.900 right at that initial assessment stage? Well, 00:03:34.020 --> 00:03:35.840 that's certainly the hope and it's an area where 00:03:35.840 --> 00:03:38.180 research is very active. We are seeing applications 00:03:38.180 --> 00:03:40.639 of AI machine learning, deep learning being used 00:03:40.639 --> 00:03:43.919 to analyze orthopedic images like x -rays. Some 00:03:43.919 --> 00:03:46.180 studies mentioned in the sources suggest AI models 00:03:46.180 --> 00:03:48.710 can perform well. Comparably to or sometimes 00:03:48.710 --> 00:03:52.069 in specific tasks maybe even outperform clinicians 00:03:52.069 --> 00:03:54.469 or radiologists really outperform in what sort 00:03:54.469 --> 00:03:56.750 of tasks things like detecting certain types 00:03:56.750 --> 00:03:59.689 of fractures or identifying and staging how Severe 00:03:59.689 --> 00:04:03.129 osteoarthritis is on a standard 2d x -ray deep 00:04:03.129 --> 00:04:05.469 learning DL. It's a type of machine learning. 00:04:05.550 --> 00:04:07.189 It's particularly good with large amounts of 00:04:07.189 --> 00:04:09.310 image data That sounds like it could really speed 00:04:09.310 --> 00:04:11.550 things up at the front end. Are there specific 00:04:11.550 --> 00:04:14.969 examples in the sources of this kind of AI analysis 00:04:14.969 --> 00:04:18.209 being used? Yes, yeah. One source mentions a 00:04:18.209 --> 00:04:22.160 deep neural network, a DNN. which is just another 00:04:22.160 --> 00:04:24.920 term for a deep learning model that Lim and Colley's 00:04:24.920 --> 00:04:28.180 developed specifically for osteoarthritis screening. 00:04:28.500 --> 00:04:31.360 Okay. Another study by Ratzlaff used web -based 00:04:31.360 --> 00:04:33.920 questionnaires combined with some AI analysis 00:04:33.920 --> 00:04:37.339 for initial OA detection. The idea is tools that 00:04:37.339 --> 00:04:39.899 could potentially screen patients earlier or 00:04:39.899 --> 00:04:42.459 maybe help generate a list of possible diagnoses 00:04:42.459 --> 00:04:45.189 to investigate. Okay, but is that... Is that 00:04:45.189 --> 00:04:48.430 really ready for prime time, though? Can AI actually 00:04:48.430 --> 00:04:51.069 diagnose someone just from, say, an image or 00:04:51.069 --> 00:04:53.589 a questionnaire remotely? Well, this is where 00:04:53.589 --> 00:04:56.649 the sources give us a bit of a reality check. 00:04:56.670 --> 00:04:58.810 Right. Because while these screening tools might 00:04:58.810 --> 00:05:01.269 show high sensitivity, they're good at flagging 00:05:01.269 --> 00:05:02.730 potential issues, so they don't miss things. 00:05:02.829 --> 00:05:04.389 So they catch a lot. They catch a lot, yeah. 00:05:04.430 --> 00:05:06.930 But their specificity can be quite low. For instance, 00:05:07.050 --> 00:05:09.930 that JamR review highlights a Neoway web questionnaire 00:05:09.930 --> 00:05:13.129 with 73 % sensitivity. It sounds good. But it 00:05:13.129 --> 00:05:16.129 also notes symptom checker specificity could 00:05:16.129 --> 00:05:20.750 be as low as, say, 23 % to 27 % in one study 00:05:20.750 --> 00:05:22.930 they looked at. Ah, OK. So high sensitivity, 00:05:23.129 --> 00:05:25.009 low specificity means? You get a lot of false 00:05:25.009 --> 00:05:27.129 positives. You flag lots of people who don't 00:05:27.129 --> 00:05:28.829 actually have the condition. Right. So it might 00:05:28.829 --> 00:05:30.649 send perfectly healthy people down the pathway 00:05:30.649 --> 00:05:34.089 for more checks. Precisely. And that JMI review 00:05:34.089 --> 00:05:36.980 is pretty clear. Despite these developments, 00:05:37.620 --> 00:05:39.959 there's actually limited evidence that current 00:05:39.959 --> 00:05:43.660 standalone digital health apps can reliably establish 00:05:43.660 --> 00:05:47.060 a primary diagnosis remotely, especially when 00:05:47.060 --> 00:05:49.060 you compare them to traditional clinical methods. 00:05:49.259 --> 00:05:51.360 Like seeing a doctor face -to -face or having 00:05:51.360 --> 00:05:53.959 a specialist review detailed scans. Exactly. 00:05:54.220 --> 00:05:56.579 And why is that limitation so significant? What 00:05:56.579 --> 00:05:59.100 are these digital tools missing compared to, 00:05:59.100 --> 00:06:02.399 say, a clinic visit? Well, they often lack the 00:06:02.399 --> 00:06:05.740 integration of crucial clinical data. A doctor 00:06:05.740 --> 00:06:07.899 gathers information from a physical exam, right? 00:06:08.220 --> 00:06:10.180 They interpret imaging, but in the full context 00:06:10.180 --> 00:06:12.620 of the patient's history, maybe order lab tests. 00:06:12.740 --> 00:06:15.339 The whole picture. The whole picture. Current 00:06:15.339 --> 00:06:17.939 remote apps often operate in silos, maybe just 00:06:17.939 --> 00:06:21.339 using one or two data streams. Plus, as the Psycho2J 00:06:21.339 --> 00:06:23.980 review points out, there's often a mismatch between 00:06:23.980 --> 00:06:26.680 how bad arthritis looks on an x -ray and the 00:06:26.680 --> 00:06:29.209 patient's actual symptoms. Oh, right. You can 00:06:29.209 --> 00:06:31.629 have a bad x -ray but feel OK or vice versa. 00:06:32.050 --> 00:06:34.870 Exactly. So an AI relying only on image severity 00:06:34.870 --> 00:06:37.970 could easily miss or misinterpret that clinical 00:06:37.970 --> 00:06:41.689 nuance. So OK. While AI offers, let's say, tantalizing 00:06:41.689 --> 00:06:44.009 potential for initial sifting or providing some 00:06:44.009 --> 00:06:46.470 info, it's definitely not replacing the full 00:06:46.470 --> 00:06:48.910 diagnostic process yet. It needs more data input, 00:06:49.290 --> 00:06:52.189 more context. Precisely. Based on these sources, 00:06:52.389 --> 00:06:54.610 the current clinical utility for standalone remote 00:06:54.610 --> 00:06:57.209 diagnosis seems quite limited. Okay, that's clear. 00:06:57.389 --> 00:06:59.269 Let's move a bit further into those early stages 00:06:59.269 --> 00:07:02.189 then, specifically preoperative planning. How's 00:07:02.189 --> 00:07:04.350 AI being used there to help surgeons get ready 00:07:04.350 --> 00:07:06.290 for a procedure? Now this is an area showing 00:07:06.290 --> 00:07:08.750 I think stronger practical application today. 00:07:09.350 --> 00:07:11.490 AI is being integrated into software that really 00:07:11.490 --> 00:07:13.689 helps with surgical preparation. Things like 00:07:13.689 --> 00:07:16.680 helping optimize implant design. There's mention 00:07:16.680 --> 00:07:19.199 of a machine learning application for hip stem 00:07:19.199 --> 00:07:22.100 design, for instance, or facilitating patient 00:07:22.100 --> 00:07:24.680 -specific adjustments, creating really detailed 00:07:24.680 --> 00:07:27.199 3D templates based on the patient's imaging data. 00:07:27.399 --> 00:07:30.759 So tailoring the plan to the individual. Exactly. 00:07:31.180 --> 00:07:33.220 One study used deep learning to identify the 00:07:33.220 --> 00:07:36.439 best anatomical landmarks for a total knee replacement, 00:07:37.000 --> 00:07:38.779 helping surgeons plan their cuts and implant 00:07:38.779 --> 00:07:41.259 placement more accurately. And we're also seeing 00:07:41.259 --> 00:07:43.600 AI used on post -operative images to predict 00:07:43.600 --> 00:07:46.439 future risks. There's a fascinating example of 00:07:46.439 --> 00:07:48.740 the sources about using a convolutional neural 00:07:48.740 --> 00:07:51.839 network, a CNN. which is great for image analysis. 00:07:52.439 --> 00:07:55.060 That achieved 89 % sensitivity in classifying 00:07:55.060 --> 00:07:57.180 total hip replacement patients for their risk 00:07:57.180 --> 00:08:00.019 of dislocation just based on a single post -op 00:08:00.019 --> 00:08:03.300 x -ray. Wow, 89%. That's very specific. What's 00:08:03.300 --> 00:08:06.180 the actual practical value of predicting dislocation 00:08:06.180 --> 00:08:08.819 risk like that from an x -ray? Well, if you can 00:08:08.819 --> 00:08:10.939 reliably identify patients predicted to be low 00:08:10.939 --> 00:08:13.240 risk, you could potentially tailor their post 00:08:13.240 --> 00:08:15.839 -operative protocols. So not everyone needs the 00:08:15.839 --> 00:08:18.660 same strict rules afterwards. Potentially, yes. 00:08:19.199 --> 00:08:21.819 Instead of a one -size -fits -all approach, maybe 00:08:21.819 --> 00:08:23.839 lower -risk patients could have fewer movement 00:08:23.839 --> 00:08:26.779 restrictions. That might lead to faster or perhaps 00:08:26.779 --> 00:08:29.639 less burdensome recoveries, while of course making 00:08:29.639 --> 00:08:31.699 sure the high -risk patients get all the necessary 00:08:31.699 --> 00:08:34.220 precautions. Right. It's about personalizing 00:08:34.220 --> 00:08:37.720 that post -op care using data to assess risk. 00:08:37.940 --> 00:08:41.360 Exactly. Data -informed risk assessment. So from 00:08:41.360 --> 00:08:44.539 refining the actual implants to patient -specific 00:08:44.539 --> 00:08:48.080 3D planning and even predicting post -op risks 00:08:48.080 --> 00:08:50.820 during planning, AI seems to be really pushing 00:08:50.820 --> 00:08:53.220 towards making surgical strategies more precise 00:08:53.220 --> 00:08:55.659 and personalized. I think that's a fair summary, 00:08:55.860 --> 00:08:57.679 yes. OK, let's shift gears a bit now. Moving 00:08:57.679 --> 00:08:59.620 beyond the planning phase into the actual treatment 00:08:59.620 --> 00:09:02.500 and then the post -operative journey, what role 00:09:02.500 --> 00:09:04.759 are AI and other digital tools playing there? 00:09:05.000 --> 00:09:07.120 Well, this is where we see AI really heavily 00:09:07.120 --> 00:09:09.279 involved in predictive analytics. predicting 00:09:09.279 --> 00:09:11.740 patient outcomes, predicting risks. The sources 00:09:11.740 --> 00:09:13.700 detail quite a few models being developed and 00:09:13.700 --> 00:09:16.580 tested for specific complications or maybe resource 00:09:16.580 --> 00:09:18.700 needs. Like what kind of things? For instance, 00:09:19.080 --> 00:09:21.200 predicting the need for blood transfusions after 00:09:21.200 --> 00:09:24.539 a total knee replacement. That's discussed with 00:09:24.539 --> 00:09:27.799 one machine learning model achieving an AUC of 00:09:27.799 --> 00:09:30.899 0 .88. AUC area under the curve. That's a measure 00:09:30.899 --> 00:09:33.259 of accuracy, right? Yes. Essentially how well 00:09:33.259 --> 00:09:36.039 the model distinguishes between groups. In this 00:09:36.039 --> 00:09:39.279 case, patients who will need a transfusion. versus 00:09:39.279 --> 00:09:42.240 those who won't. 0 .88 is considered pretty good 00:09:42.240 --> 00:09:44.399 performance. And these predictive models, they're 00:09:44.399 --> 00:09:48.059 also being used for sort of operational efficiency 00:09:48.059 --> 00:09:51.440 too, like predicting how long someone might stay 00:09:51.440 --> 00:09:53.720 in hospital or if they might be readmitted. Oh, 00:09:53.759 --> 00:09:56.340 absolutely. Predicting length of stay after procedures 00:09:56.340 --> 00:09:58.960 like total hip replacement is a key area. Same 00:09:58.960 --> 00:10:01.080 for predicting unplanned readmissions. Right. 00:10:01.779 --> 00:10:03.659 And these predictions aren't just useful clinically. 00:10:03.840 --> 00:10:06.740 The BOA source specifically highlights how predictive 00:10:06.740 --> 00:10:09.320 analytics can really help improve hospital efficiency 00:10:09.320 --> 00:10:12.519 and resource management. Forecasting demand, 00:10:13.080 --> 00:10:14.860 managing patient flow better. Makes sense. They 00:10:14.860 --> 00:10:17.159 even mentioned studies showing high accuracy 00:10:17.159 --> 00:10:20.039 for predicting things like how long a specific 00:10:20.039 --> 00:10:22.950 surgical case might take. And this predictive 00:10:22.950 --> 00:10:25.970 capability also extends to quite serious complications. 00:10:26.710 --> 00:10:29.029 Things like predicting periprosthetic joint infection 00:10:29.029 --> 00:10:32.970 PJI or surgical site infections after knee replacements. 00:10:33.409 --> 00:10:35.750 That allows for more targeted prevention efforts 00:10:35.750 --> 00:10:39.350 in those identified as high risk. OK, predicting 00:10:39.350 --> 00:10:42.029 objective clinical outcomes like infection or 00:10:42.029 --> 00:10:44.750 length of stay, that makes intuitive sense. But 00:10:44.750 --> 00:10:47.590 some sources mentioned predicting patient satisfaction. 00:10:47.809 --> 00:10:50.830 That seems. Well, much harder to pin down and 00:10:50.830 --> 00:10:53.210 predict. It is definitely complex. And the sources 00:10:53.210 --> 00:10:55.490 acknowledge that even with technically successful 00:10:55.490 --> 00:10:58.669 surgery, a significant chunk of patients, maybe 00:10:58.669 --> 00:11:01.110 20%, sometimes even 30 % report they haven't 00:11:01.110 --> 00:11:03.289 really improved, or they're actually dissatisfied 00:11:03.289 --> 00:11:05.690 after a joint replacement. Wow, that high. It 00:11:05.690 --> 00:11:07.850 can be, yes. What's interesting is that machine 00:11:07.850 --> 00:11:09.549 learning models are being developed to try and 00:11:09.549 --> 00:11:12.570 predict this dissatisfaction. And often, factors 00:11:12.570 --> 00:11:14.610 like the patient's preoperative functional level 00:11:14.610 --> 00:11:17.549 and their mental health status emerge as really 00:11:17.549 --> 00:11:20.149 key influencers. So it's not just about the surgery 00:11:20.149 --> 00:11:23.429 itself? Not entirely, no. It highlights that 00:11:23.429 --> 00:11:26.570 satisfaction is multifaceted. One study mentioned 00:11:26.570 --> 00:11:29.529 used a multimodal model pulling in various data 00:11:29.529 --> 00:11:33.629 points and achieved an AUC of 0 .816 in predicting 00:11:33.629 --> 00:11:36.429 dissatisfaction. But thinking about the data 00:11:36.429 --> 00:11:39.610 used for these complex predictions, can we really 00:11:39.610 --> 00:11:42.090 rely heavily on things like patient reported 00:11:42.090 --> 00:11:45.210 outcome measures PROMs? Are they robust enough 00:11:45.210 --> 00:11:47.539 on their own? That's a really important point 00:11:47.539 --> 00:11:50.000 and the sources do caution against relying solely 00:11:50.000 --> 00:11:52.639 on PROMs for prediction. They can suffer from 00:11:52.639 --> 00:11:54.740 what's called a ceiling effect. Meaning? Meaning 00:11:54.740 --> 00:11:56.679 patients who already have relatively high function 00:11:56.679 --> 00:11:59.360 before surgery might not show a large measured 00:11:59.360 --> 00:12:02.269 improvement on a problem score afterwards, even 00:12:02.269 --> 00:12:04.830 if they personally feel the surgery was a huge 00:12:04.830 --> 00:12:07.809 success for them. Ah, I see. The scale doesn't 00:12:07.809 --> 00:12:10.029 capture their perceived benefit. Exactly. It 00:12:10.029 --> 00:12:12.350 points to needing richer, perhaps more nuanced 00:12:12.350 --> 00:12:15.149 data beyond just problem scores to get really 00:12:15.149 --> 00:12:16.909 accurate predictions, especially for something 00:12:16.909 --> 00:12:19.470 like satisfaction. And this whole area of predictive 00:12:19.470 --> 00:12:22.070 power, especially around risk, it brings up quite 00:12:22.070 --> 00:12:24.250 a significant ethical point that was raised in 00:12:24.250 --> 00:12:26.659 the sources, doesn't it? Yes, it absolutely does. 00:12:26.779 --> 00:12:29.000 It's a critical consideration. I mean, while 00:12:29.000 --> 00:12:31.679 risk stratification is helpful for tailoring 00:12:31.679 --> 00:12:35.159 care and allocating resources effectively, using 00:12:35.159 --> 00:12:37.779 predictive models to label patients based on 00:12:37.779 --> 00:12:40.480 their predicted outcomes or complication risk 00:12:40.480 --> 00:12:42.840 carries a real potential for marginalization. 00:12:42.860 --> 00:12:46.120 Yeah, so? Well, there's a genuine concern that 00:12:46.120 --> 00:12:49.019 patients who are deemed too high risk by an algorithm 00:12:49.019 --> 00:12:52.740 might be perhaps unfairly labeled as inoperable. 00:12:53.080 --> 00:12:55.960 even if they might still stand to benefit significantly 00:12:55.960 --> 00:12:58.399 from surgery. So the algorithm could effectively 00:12:58.399 --> 00:13:01.159 deny them care. That's the fear. It raises very 00:13:01.159 --> 00:13:03.220 challenging questions about equitable access 00:13:03.220 --> 00:13:06.220 to care and what role algorithm should play in 00:13:06.220 --> 00:13:08.919 fundamental clinical decisions. It's a complex 00:13:08.919 --> 00:13:11.399 ethical landscape we need to navigate very carefully 00:13:11.399 --> 00:13:13.720 as these tools get more sophisticated. Definitely 00:13:13.720 --> 00:13:16.220 something to watch. Okay, beyond the predictive 00:13:16.220 --> 00:13:19.139 AI side, what other digital tools are factoring 00:13:19.139 --> 00:13:21.830 into the treatment and monitoring phases? Well, 00:13:21.909 --> 00:13:24.509 robotic assisted surgery is obviously a key technology 00:13:24.509 --> 00:13:27.460 that's integrated into treatment itself. It's 00:13:27.460 --> 00:13:29.980 often highlighted for enhancing precision in 00:13:29.980 --> 00:13:32.419 certain procedures, particularly mentioned in 00:13:32.419 --> 00:13:34.419 spinal surgery examples in the sources. Right, 00:13:34.500 --> 00:13:37.940 the robots. And then increasingly remote monitoring 00:13:37.940 --> 00:13:40.899 tools are becoming important, especially postoperatively. 00:13:41.500 --> 00:13:44.779 So things like wearables, external sensors, and 00:13:44.779 --> 00:13:47.480 even the development of so -called smart implants. 00:13:47.820 --> 00:13:50.000 Smart implants? That sounds very futuristic. 00:13:50.220 --> 00:13:52.059 What sort of data are they actually providing? 00:13:52.320 --> 00:13:54.419 Well... They're becoming much more of a reality. 00:13:54.860 --> 00:13:57.059 The digitalization review we looked at mentions 00:13:57.059 --> 00:13:59.980 smart implants being explored in areas like total 00:13:59.980 --> 00:14:03.519 knee replacements or for monitoring how well 00:14:03.519 --> 00:14:06.220 spinal fusion is healing. The idea is they can 00:14:06.220 --> 00:14:08.980 provide real -time data on things like the biomechanical 00:14:08.980 --> 00:14:11.320 load actually being placed on the implant. Inside 00:14:11.320 --> 00:14:13.639 the body. Inside the body, yes. Or the status 00:14:13.639 --> 00:14:16.220 of bone healing around it, potentially even detecting 00:14:16.220 --> 00:14:18.740 early signs of the implant loosening or maybe 00:14:18.740 --> 00:14:21.940 the formation of infection biofilms. Really granular 00:14:21.940 --> 00:14:24.490 data. Fascinating. And what about the more familiar 00:14:24.490 --> 00:14:27.149 things like wearables or external sensors? Yeah, 00:14:27.149 --> 00:14:29.429 they're being used quite a bit to track patient 00:14:29.429 --> 00:14:31.970 activity levels, their gait patterns, range of 00:14:31.970 --> 00:14:34.549 motion during recovery. Wearables have actually 00:14:34.549 --> 00:14:36.889 been validated for accurately monitoring these 00:14:36.889 --> 00:14:39.450 sorts of parameters post -operatively for things 00:14:39.450 --> 00:14:41.250 like knee and shoulder surgeries. And the main 00:14:41.250 --> 00:14:44.809 benefit there is? The major benefit is enabling 00:14:44.809 --> 00:14:48.350 effective remote recovery monitoring. It allows 00:14:48.350 --> 00:14:51.330 clinicians to potentially tailor rehab plans 00:14:51.519 --> 00:14:54.539 based on real data and maybe identify complications 00:14:54.539 --> 00:14:57.320 earlier without needing so many frequent in -person 00:14:57.320 --> 00:14:59.940 appointments. Right. Convenience and potentially 00:14:59.940 --> 00:15:02.759 better oversight. That's the goal. OK. So all 00:15:02.759 --> 00:15:05.340 of this you've got. imaging data, clinical notes, 00:15:05.700 --> 00:15:08.940 sensor data from wearables, patient reports like 00:15:08.940 --> 00:15:11.500 PROMs, maybe even data streaming from a smart 00:15:11.500 --> 00:15:13.639 implant. That generates just an immense amount 00:15:13.639 --> 00:15:15.720 of information, doesn't it? How do orthopedic 00:15:15.720 --> 00:15:17.940 systems even begin to make sense of that, that 00:15:17.940 --> 00:15:20.379 kind of fire hose of data? That is precisely 00:15:20.379 --> 00:15:22.879 the major hurdle we touched on earlier. Data 00:15:22.879 --> 00:15:25.840 integration. It's huge. And as the Digital Twins' 00:15:26.000 --> 00:15:28.879 TED Talk highlighted, data from complex systems 00:15:28.879 --> 00:15:32.340 like a human body undergoing recovery is often, 00:15:32.559 --> 00:15:36.210 well, sparse in some ways, noisy in others, sometimes 00:15:36.210 --> 00:15:39.110 indirect. And it often only gives you a snapshot 00:15:39.110 --> 00:15:41.889 of now. It doesn't automatically tell you what's 00:15:41.889 --> 00:15:44.049 going to happen next or why something is happening. 00:15:44.190 --> 00:15:46.590 So just having more data isn't necessarily the 00:15:46.590 --> 00:15:49.169 answer on its own. Not necessarily no. Yeah. 00:15:49.250 --> 00:15:51.110 And this is where the concept of a digital twin 00:15:51.110 --> 00:15:53.570 comes in as a potential maybe future direction. 00:15:53.789 --> 00:15:55.710 A digital twin. Explain that a bit more. OK. 00:15:55.730 --> 00:15:58.110 So the idea is combining predictive models. These 00:15:58.110 --> 00:16:00.730 could be models based on physics. like how joints 00:16:00.730 --> 00:16:03.509 biomechanically move, or models learned from 00:16:03.509 --> 00:16:06.269 data using AI combining those models with something 00:16:06.269 --> 00:16:08.809 called data assimilation. Data assimilation. 00:16:08.909 --> 00:16:10.809 Yeah, it basically means continuously feeding 00:16:10.809 --> 00:16:13.590 that model new real -time data from the actual 00:16:13.590 --> 00:16:16.850 patient. So sensor data, maybe PROMs they submit, 00:16:17.309 --> 00:16:19.230 perhaps even aspects of their lifestyle tracked 00:16:19.230 --> 00:16:21.870 via an app. You feed that data in constantly. 00:16:21.950 --> 00:16:24.899 To keep the model updated. Exactly, to keep the 00:16:24.899 --> 00:16:28.039 digital model of that specific patient or maybe 00:16:28.039 --> 00:16:31.059 their implant dynamic and current and reflective 00:16:31.059 --> 00:16:33.940 of their reality. So you're building a constantly 00:16:33.940 --> 00:16:37.360 updating digital replica of the patient's orthopedic 00:16:37.360 --> 00:16:39.539 situation. That's a good way to put it, yes. 00:16:39.840 --> 00:16:43.120 Applying this concept in orthopedics could potentially... 00:16:43.080 --> 00:16:46.039 create a really comprehensive, integrated digital 00:16:46.039 --> 00:16:48.440 representation of the patient. It could give 00:16:48.440 --> 00:16:51.399 clinicians a much deeper, more dynamic understanding 00:16:51.399 --> 00:16:54.279 than static records usually allow. Leading to 00:16:54.279 --> 00:16:56.320 better predictions and more informed decisions, 00:16:56.539 --> 00:16:59.039 presumably. That's the hope, but... And it's 00:16:59.039 --> 00:17:02.320 a big but. Making this work requires establishing 00:17:02.320 --> 00:17:06.460 reliable, connected clinical databases, the whole 00:17:06.460 --> 00:17:09.279 big data infrastructure. And it needs pretty 00:17:09.279 --> 00:17:11.119 unprecedented collaboration across different 00:17:11.119 --> 00:17:13.400 disciplines. You need the clinicians, the data 00:17:13.400 --> 00:17:15.680 scientists, legal experts for privacy and security. 00:17:16.000 --> 00:17:17.599 And crucially, you need the patients involved, 00:17:17.839 --> 00:17:19.779 too. And that brings us right back to the patient, 00:17:19.900 --> 00:17:22.559 doesn't it? Many of these tools, especially the 00:17:22.559 --> 00:17:24.519 monitoring apps, the data collection devices, 00:17:25.079 --> 00:17:27.380 they require the patient to actively participate. 00:17:27.559 --> 00:17:30.200 And we know getting people to consistently use 00:17:30.200 --> 00:17:32.700 health care apps can be a real challenge. It 00:17:32.700 --> 00:17:37.009 really, really can be. maybe insights from completely 00:17:37.009 --> 00:17:40.230 different areas like the Duolingo TED Talk become 00:17:40.230 --> 00:17:42.470 relevant. The language learning app, how's that 00:17:42.470 --> 00:17:44.750 relevant? Well, the core idea discussed there 00:17:44.750 --> 00:17:47.849 is about applying psychological techniques that 00:17:47.849 --> 00:17:51.170 consumer tech uses very effectively. Things like 00:17:51.170 --> 00:17:54.430 gamification, smart notifications, visual progress 00:17:54.430 --> 00:17:57.609 trackers. Making it engaging. Exactly. Not to 00:17:57.609 --> 00:17:59.109 make healthcare addictive like social media, 00:17:59.390 --> 00:18:01.819 obviously, but to make... Using the necessary 00:18:01.819 --> 00:18:04.539 tools, tracking your rehab exercises, reporting 00:18:04.539 --> 00:18:07.059 your pain levels, allowing sensors to collect 00:18:07.059 --> 00:18:09.819 data to make doing those things consistently 00:18:09.819 --> 00:18:12.359 more engaging, maybe even rewarding. Sort of 00:18:12.359 --> 00:18:14.519 making the broccoli taste like dessert, as the 00:18:14.519 --> 00:18:16.579 speaker put it. Making the healthy, necessary 00:18:16.579 --> 00:18:19.039 thing feel a bit more appealing. Precisely that 00:18:19.039 --> 00:18:22.450 analogy. For orthopedics, it means thinking hard 00:18:22.450 --> 00:18:25.170 about designing apps or integrating wearables 00:18:25.170 --> 00:18:27.890 in a way that genuinely encourages patients to 00:18:27.890 --> 00:18:30.509 stick with it, to track their progress, report 00:18:30.509 --> 00:18:32.910 how they're feeling, allow the data collection. 00:18:33.309 --> 00:18:35.490 Because the best remote monitoring tech in the 00:18:35.490 --> 00:18:38.569 world is useless if the patient stops using it 00:18:38.569 --> 00:18:41.529 after a week. Patient buy -in and continued engagement 00:18:41.529 --> 00:18:44.109 is absolutely essential for the data flow these 00:18:44.109 --> 00:18:46.470 advanced systems need. Couldn't agree more. It's 00:18:46.470 --> 00:18:48.680 critical. So let's try and synthesize where we 00:18:48.680 --> 00:18:51.539 stand then, according to these sources. The potential 00:18:51.539 --> 00:18:54.480 for digital tech and AI in orthopedics looks, 00:18:54.740 --> 00:18:57.920 well, immense. It seems to cover the entire patient 00:18:57.920 --> 00:19:00.259 journey, right from that first contact all the 00:19:00.259 --> 00:19:02.500 way through recovery. I think that's right. There's 00:19:02.500 --> 00:19:05.299 clearly significant promise for improving efficiency, 00:19:05.559 --> 00:19:07.460 for personalizing treatment pathways, enhancing 00:19:07.460 --> 00:19:10.200 safety protocols, and potentially improving patient 00:19:10.200 --> 00:19:12.380 outcomes, especially facing that rising demand 00:19:12.380 --> 00:19:14.740 we talked about. But in it feels like a significant, 00:19:15.019 --> 00:19:16.880 but running through the sources, many of these 00:19:16.880 --> 00:19:18.519 applications are still very much in development 00:19:18.519 --> 00:19:21.180 or maybe in the early stages of validation. Yes, 00:19:21.180 --> 00:19:25.400 absolutely. Widespread routine adoption requires 00:19:25.400 --> 00:19:28.339 more robust clinical evidence for many applications. 00:19:28.839 --> 00:19:30.599 It needs standardized data protocols. We need 00:19:30.599 --> 00:19:33.299 to overcome those significant data integration 00:19:33.299 --> 00:19:36.160 challenges. We need to build trust and very importantly, 00:19:36.180 --> 00:19:38.640 navigate those ethical considerations carefully 00:19:38.640 --> 00:19:42.099 to ensure fair and equitable access and application. 00:19:42.269 --> 00:19:44.230 Lots of potential, lots of work still to do. 00:19:44.829 --> 00:19:46.829 Okay, fascinating insights. Let's finish off 00:19:46.829 --> 00:19:48.890 with our lightning round quick questions, sharp 00:19:48.890 --> 00:19:52.690 answers if you can. Based on these sources, what's 00:19:52.690 --> 00:19:55.150 the single piece of digital tech that orthopedic 00:19:55.150 --> 00:19:56.910 professionals should really be paying closest 00:19:56.910 --> 00:19:59.630 attention to in the next, say, one to two years? 00:20:00.130 --> 00:20:02.839 I'd say prediction models. Specifically, models 00:20:02.839 --> 00:20:04.839 that move beyond using just one type of data 00:20:04.839 --> 00:20:07.400 and start integrating clinical information, imaging 00:20:07.400 --> 00:20:09.819 data, and potentially sensor data together for 00:20:09.819 --> 00:20:11.920 more holistic prediction. Okay, integrated prediction 00:20:11.920 --> 00:20:14.400 models. Beyond the technology itself, what's 00:20:14.400 --> 00:20:16.740 the biggest non -technical challenge to successful 00:20:16.740 --> 00:20:19.220 adoption of all these tools? For me, it has to 00:20:19.220 --> 00:20:23.339 be establishing that robust, secure, and genuinely 00:20:23.339 --> 00:20:26.599 integrated data infrastructure across different 00:20:26.599 --> 00:20:29.220 parts of the healthcare system. It underpins 00:20:29.400 --> 00:20:31.579 Everything else. The data infrastructure. Got 00:20:31.579 --> 00:20:35.099 it. And finally, what's one small, maybe actionable 00:20:35.099 --> 00:20:36.940 step an orthopedic professional listening could 00:20:36.940 --> 00:20:39.339 take today based on what we've discussed? I think 00:20:39.339 --> 00:20:42.460 a useful step would be to actively explore the 00:20:42.460 --> 00:20:44.579 current state of data collection and accessibility 00:20:44.579 --> 00:20:47.480 within their own clinical environment. Just understand 00:20:47.480 --> 00:20:51.119 what data exists, how accessible it is, and identify 00:20:51.119 --> 00:20:53.680 potential opportunities for maybe better integration 00:20:53.680 --> 00:20:55.819 or better utilization of the data sources they 00:20:55.819 --> 00:20:57.720 already have. Start by understanding your own 00:20:57.720 --> 00:21:01.029 data landscape. Excellent. OK, let's quickly 00:21:01.029 --> 00:21:03.190 recap the key takeaways from this deep dive for 00:21:03.190 --> 00:21:06.170 you. Good idea. First, digital technology, especially 00:21:06.170 --> 00:21:09.130 AI, it isn't just a nice to have anymore. The 00:21:09.130 --> 00:21:11.029 sources really frame it as becoming essential 00:21:11.029 --> 00:21:13.309 if we're going to manage the growing global demand 00:21:13.309 --> 00:21:16.029 in orthopedics. Unavoidable, really. Second, 00:21:16.359 --> 00:21:18.960 AI is showing practical promise right across 00:21:18.960 --> 00:21:21.599 that patient journey, improving image analysis, 00:21:21.920 --> 00:21:24.480 helping plan surgeries more precisely, and getting 00:21:24.480 --> 00:21:26.579 better at predicting patient outcomes and risks. 00:21:26.779 --> 00:21:29.839 Yes, definite progress there. Third, while digital 00:21:29.839 --> 00:21:32.220 tools offer some screening potential, they currently 00:21:32.220 --> 00:21:34.359 have clear limitations for standalone remote 00:21:34.359 --> 00:21:37.500 diagnosis. That highlights the absolute need 00:21:37.500 --> 00:21:40.500 to integrate multiple data sources, the exam 00:21:40.500 --> 00:21:44.420 findings, labs, imaging, sensor data, not just 00:21:44.420 --> 00:21:47.680 rely on one isolate. piece. Crucial point, that 00:21:47.680 --> 00:21:50.039 integration. Fourth, making all this actually 00:21:50.039 --> 00:21:53.019 work in practice requires building robust, secure 00:21:53.019 --> 00:21:56.119 data infrastructure. And we have to tackle significant 00:21:56.119 --> 00:21:58.380 non -technical hurdles like data integration 00:21:58.380 --> 00:22:01.819 itself, validation, building trust, and ensuring 00:22:01.819 --> 00:22:04.599 equitable access for all patients. Big challenges, 00:22:04.839 --> 00:22:06.960 but necessary ones. And finally, for those tools 00:22:06.960 --> 00:22:09.720 that need active patient participation, the apps, 00:22:09.980 --> 00:22:11.619 the wearables, we really need to think about 00:22:11.619 --> 00:22:14.500 how to genuinely engage users. Maybe borrowing 00:22:14.500 --> 00:22:16.660 successful techniques from other digital fields 00:22:16.660 --> 00:22:18.779 is key if these tools are going to deliver their 00:22:18.779 --> 00:22:21.599 full value. Engagement is key, definitely. If 00:22:21.599 --> 00:22:23.839 you found this deep dive valuable, please do 00:22:23.839 --> 00:22:25.680 take a moment to rate and share it. It really 00:22:25.680 --> 00:22:27.859 helps other professionals like you discover these 00:22:27.859 --> 00:22:30.980 insights. Please do. And as you reflect on the 00:22:30.980 --> 00:22:33.180 future of orthopedic care and the increasing 00:22:33.180 --> 00:22:35.599 power of these digital tools, here's maybe something 00:22:35.599 --> 00:22:38.809 to ponder. If AI gets really good at predicting 00:22:38.809 --> 00:22:41.410 a patient's likelihood of satisfaction or their 00:22:41.410 --> 00:22:44.069 risk of specific complications, how does that 00:22:44.069 --> 00:22:46.950 fundamentally change the nature of the conversation, 00:22:47.150 --> 00:22:49.170 that shared decision -making process that happens 00:22:49.170 --> 00:22:51.410 between the doctor and the patient in the clinic 00:22:51.410 --> 00:22:52.990 room? What does that look like?