Digital Pathology Podcast
Digital Pathology Podcast
149: Pixels to Prognosis: The Tesla of Pathology Isn't Quite Ready to Drive Itself w, Dr. Anil Parwani
In this episode sponsored by Epredia, Dr. Anil Parwani explores the transformative journey of digital pathology from basic slide scanning to AI-driven diagnostics. He shares real-world implementation experiences and demonstrates how these technologies are addressing critical challenges in pathology practice.
- Pathology faces increasing demands amid workforce shortages and knowledge explosion
- Digital pathology provides standardization, objectivity, and automation beyond glass slides
- Ohio State University has scanned 4.2 million slides representing nearly 500,000 cases since 2016
- Current AI applications include biomarker quantification, rare event detection, and tumor classification
- Integration challenges remain the primary barrier to seamless adoption of AI tools
- Future technologies include virtual staining, 3D pathology, and large language model integration
- Artificial intelligence remains task-oriented while real intelligence is context-aware and knowledge-based
- Each institution must navigate their own "digital pathology chasm" based on specific needs
- Digital tools will augment pathologists' capabilities rather than replace human expertise
- The technology marketplace offers solutions for every stage of the digital transformation journey
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What happens when academic pathology, technology and innovation converge. In this talk, dr Anil Parwani shares how his team is using AI, machine learning and digital tools to transform surgical pathology workflows from scanning to reporting to education. If you've ever wondered what it takes to implement digital pathology at scale in a hospital setting, or how AI is practically used in diagnostics today, this presentation delivers clear, real-world answers.
Speaker 2:Learn about the newest digital pathology trends in science and industry. Meet the most interesting people in the niche and gain insights relevant to your own projects. Here is where pathology meets computer science. You are listening to the Digital Pathology Podcast with your host, Dr Aleksandr Zhurov.
Speaker 3:Welcome to the morning event. I'm going to talk to you today about the journey of digital pathology. It's different for everyone. Some people in the audience are looking for their first scanner, right? Some of us are looking at the first AI implementation at our institutes. Others are looking beyond that. So it's really many different journeys and when I speak to attendees, they share their stories. What are their pain points, what are the things that they're looking forward to solving with their journey. So how many of you are buying a new scanner? A few of them. How many of you have completely moved to digital? Several, several of you. Excellent Greetings from Columbus.
Speaker 3:So I want to talk to you today about these journeys, the role of digital pathology and AI, and list some applications which we can do today. But I want to go beyond the glass slide, right? So what are the barriers today which can take us beyond the glass slide? So what are the barriers today which can take us beyond the glass slide and where are we today in that journey and what are the potentials moving forward? We have powerful microscopes, whole-site imaging scanning systems, but today we have technologies which go beyond that. So I also want to talk about that. So it's really an exciting time to be in medicine and pathology. We are generating lots of data, lots of data which is now being converted into pixels, into bits and bytes, and we are making decisions on that data. We are sending the data to the electronic medical record. Patients are consuming this data in their portals. They're looking at the diagnosis, they're trying to decipher that. What does it mean for my diagnosis? So it's truly an enabler of clinical decision-making. At the end of this, when you buy your first scanner, when you implement it, the goal is how does it transform your pathology practice? Right? So that's what I want to talk to you about today.
Speaker 3:So, as pathologists, we play a critical role in cancer diagnosis. Number of cancer cases continue to go up and the demand and complexity of our services keep growing. The workforce is continuing to shrink, there is a critical shortage of pathologists and lab staff and we are learning more about disease than ever before, right? So all these combined combinations of shortage of pathologists burnout new knowledge. It requires some disruption, it requires a new way of finding things, a new way of discovery and also a new way of making and enabling diagnosis. So, as pathologists, we perform many tasks, many manual tasks. We count things, we annotate information, we assemble information. At the end, what is our report? The report is our endpoint, but it's a continuum in the patient's journey, right?
Speaker 3:So if I look at all the challenges in pathology today, which is lack of standardization, subjectivity, many manual processes, there are some labs even today which don't even have barcoding. We were at the ASDP session yesterday and they did a survey which showed a lot of the labs around the world don't even have basic technologies. I mean, we take things for granted here, but globally there is a shortage of pathologists. Pathologists are overworked, right? How many pathologists here are for vacation? They're here to play golf, right? So if you ask pathologists in general, they will tell you we are overworked, right? I see my friend Masood in the audience there from a private practice and he's always complaining we're looking for pathologists, we have more work than we can do, right? So? And there is an explosion of medical knowledge.
Speaker 3:So what does digital pathology potentially provide us? Going beyond the glass slide, is standardization, more objectivity, more automation, more accuracy. Maybe you can do it faster If you're first starting out. You're signing out your first few cases. You might say I'm slow on the digital, but once you overcome that, you will see the difference In terms of why today, why now, why in 2024? So the current environment is suited for digital pathology and AI models.
Speaker 3:Ten years ago, we used to ask the question which scanner to buy? Right? Many people used to ask that question. We now have cost-effective, high-performance computing, which is cheaper, which is more readily available. Many academic centers have supercomputers, so we now have that environment which is needed for growth of AI. We also have better algorithms, right, so algorithms have continued to evolve and they have become commercial grade. There are some which are FDA approved, and then you have more data than ever before, right? So if you look around the room and if you ask each one of you collectively within this room, we have several million images scanned, maybe more than 100 million images scanned globally. Right, so we have experience. We have data. Is that data easily available and shareable today? No, but we are getting to that point, right, we have several collaborative networks and organizations that are working on it. We have more. We have FDA-approved algorithms, for example, for prostate cancer. We have clearly the cost of making an H&E slide.
Speaker 3:Digital has continued to go down and adoption has continued to increase, right, so we have really evolved from early demonstration in the telepathology in the 1980s to 2024, where we have several labs which are digital and have implemented AI, and if you walk around the exhibit area you can see all these products. It amazes me. I've been coming to this conference for 10 years and every year there is continuous innovations and developments in this area, so it's an exciting time. If you think about, where are we, have we reached the ceiling in terms of making a glass slide digital? So I think there are still innovations that need to happen. There are specialized areas cytopathology, for example, hematopathology where we need different types of scanning devices. Maybe Z stacking, maybe alternative-stacking, maybe alternative light sources, I don't know.
Speaker 3:We continue to have to continue to evolve and innovate the whole slide scanning systems, but we can go beyond that, right. So today you have a suite of scanning products out there. If you go to the exhibit area, you can see many of these in action and they can make slides into digital images. They can do immunofluorescence, they can even do polarization now. So this is a given right. So diagnostic quality images are a commodity now, right, everybody agrees, right. Who doesn't agree? Who thinks we need more work in this area? So all the vendors are in the room and there is room to grow right, but on a given day. So I'm on service today, I'm covering GU and I can look at images which are diagnostic quality and I feel confident in my diagnosis as a pathologist.
Speaker 3:We have achieved spatial samplings of 0.25 microns per pixel. We are now able to create multiplexing systems which allow us to interrogate multiple biomarkers. There was a system I saw yesterday in the Wendell area with 60 to 70 biomarkers could be interrogated on one slide. We have gone beyond the glass slide to a digital image and now we are moving forward. So collectively, this is a really strong study. It's a metadata and meta-analysis of over 2,900 AI and DP studies which looked at the from multiple countries, over 152,000 whole slide images representing not just cancer but many diseases. And if you look at overall from these studies, 100 were selected, 40 were drilled down into it 96.3% sensitivity, 93.3% specificity. So clearly there are studies which have demonstrated that glass slide and digital pathology are equivalent or digital images are non-inferior or not inferior. These studies must continue. We need to do more of these. We need to do this in diverse populations, not just very focused on one region of the country, but globally, and I think when we discuss this at the ASDP, that is an important aspect of moving these forward.
Speaker 3:So any guesses where we are? This is Orlando. Who thinks it's Orlando? This is Columbus moving these forward. So any guesses where we are? This is Orlando. Who thinks it's Orlando? This is Columbus, ohio. Yeah, so maybe this will bring some recognition. So we're excited Football season is on in Columbus right now.
Speaker 3:So I just briefly, with a few slides, want to show you where we are in this journey. We started in 2016, and we implemented. We did retrospective scanning going back 10 years. We started prospective scanning, we started primary diagnosis and now we have several pathologists who are in this room who are completely digital. We have David Kellogg here. He runs the operation and the scanning site, and I think several of our other team members are here as well. But several of the pathologists now have come to a point where, if the digital system goes down, david gets a lot of angry emails. So we've completely turned over right. When we were first adopting it, there were one or two pathologists and they were just the lone rangers, but now many pathologists are digital. David just shared this data with me yesterday. Many pathologists are digital. David just shared this data with me yesterday Almost 4.2 million slides scanned, almost half a million cases scanned.
Speaker 3:Year-to-year volume growth, you can see, continues to grow. And what I love about this is the user engagement and that's the key right. You want to create an environment where users are excited to use the system. This is a critical piece of change management right. Change management is not easy, but it'll come with user engagement. If you have two or three we had a good discussion with my colleagues from UAB yesterday about this and how do you start this journey? It's getting a few people super engaged and get this journey started.
Speaker 3:So the scan slides are instantaneously available. They're linked to our lab information system and we are continuing to add more bells and whistles to this system. Right now we move from a case-level integration. Now we have slide-level integration so we can individually call out the slides. All those 4.2 million slides are available. We can consult with colleagues easily.
Speaker 3:This, to me, has been a game changer. Our neuropathologists are in a different building. Our dermatopathologists are four or five miles away. If I have a difficult penile biopsy, I can just press a button and connect with them in real time. And frozen sections and everything else is much more easier and streamlined because we have a way to share images. We have implemented PatPresenter for our consultation work and that's now being integrated into into our system, so scan slides are instantaneously available. There is no waiting for foldering. It doesn't matter which order they are scanned in. They show up in your queue ready to be signed out, and we are starting to explore cytology preparations FNAs using small scanners on the bedside.
Speaker 3:So, in summary, digital slides have continued to improve my workflow. I love the direct interface to the LIS. I love sharing cases with consultants and colleagues and flagging cases, looking at the prior cases, so it has improved my turnaround time. Because I'm not. Our histology lab is three or five, three miles away from the main campus, so it's allowed us to share those images more easily. All right, quiz now which city is this? No, close to Columbus, though. It's Cleveland. Very close to it. So if you take 71, you have Cleveland, columbus and Cincinnati. So 71 corridor, right.
Speaker 3:So what do we do next? Right, as the systems mature, as you start your own journey of buying your own scanners, implementing it as these systems mature, how do we go beyond that right? How do we start thinking about other things which we cannot do with glass slides as adoption increases. So, if you look at a typical product adoption curve, you have innovators, you have early adopters, but many of them hit this chasm right. So every one of you have their own chasms. You might be starting your journey, you might be exploring how to implement digital pathology, but the key is finding out what is your pain point, what is your chasm? How do I implement digital pathology? But the key is finding out what is your pain point, what is your chasm? How do you go beyond this chasm? And we are facing the same thing, right, as we build, as we think about implementing AI, we have to deal with integration, we have to deal with interoperability. All these are key things. So how do you go beyond the glass slide chasm? Right? So we have established in this room we can now create diagnostic quality images. How do we go from there? Right? So what can we do with digital images that we cannot do with glass slides? So it really boils down to managing the information, sharing the images, connecting with each other, connecting with experts, but also exploiting the pixel pipeline to build algorithms or buy algorithms to identify, quantitate, synthesize and create knowledge pathways, create important clinical decision-making skills, right?
Speaker 3:This is the next part of my talk. I'm going to focus on what are some institutes doing and where are we today with this chasm? Where are we today with computational pathology and AI for clinical decision-making? So these are some of the things which I feel are already here today, in 2024, right, we have very sophisticated image analysis algorithms, ai algorithms for detection and diagnosis. Right, to analyze digital images, decipher the pixels, identify features like morphology, cell shapes, size of nuclei, architecture and staining patterns. So, pattern detection, feature detection these are things I learned as a resident, and I learned that by looking at over and over again and building an algorithm in my brain which allowed me to look at a prostate gland and say, okay, this is cancer, because A, b, c, d, e, same thing, right. So we are at a point in 2024 where many institutes have implemented AI in pathology. Right, and they started with very basic things counting cells, looking at the size of nuclei, differentiating different nuclei, differentiating positive versus negative signals. So all these focus on biomarkers which could be diagnostic or predictive. Right, so these could include detecting, classifying, segmenting, quantifying and localization. Here are the four applications that we are starting to use in our Institute. And again, we have our own chasm. Our chasm is that we cannot readily integrate many of these algorithms. So we still have to go to a third party, launch the algorithm and use the algorithm and bring this information back to the LIS. In an ideal world, in my wish list, we want this to be completely integrated and that's where we are heading towards.
Speaker 3:But quantitative digital image analysis for biomarkers this is very, very routinely done. Now there is a separate CPT code for this. You can actually get paid a little bit more, maybe, I don't know twenty, thirty dollars more for a digital image analysis system. If you use in your lab, it's more objective, more accurate, more faster. But is it easy to do? Is it cheap? So the answer is it is easy to do. It's easier to do if you already have digital slides in your system.
Speaker 3:It may not be the first application you launch in your lab, right? If you look at this gastric neuroendocrine tumor and you can see a 2-millimeter square area was analyzed in 28 seconds. So if I asked each one of you in the room to count the blue dots and I give you three seconds, you all will have a different answer. Right? Everybody agree with that hypothesis. And if I showed you this, it's gonna be even harder, right, but we can get this data objectively and it's reproducible every time you do it, if the answer will be the same in Cleveland or in Columbus, as long as you're using the same algorithm and you've validated it. It's a locked system and so on. These are a given.
Speaker 3:Other type of algorithms we are using is detecting rare events. This is a lymph node detection algorithm, right, so we can actually launch it directly from the viewer today. But we still have to do a lot of manual work to get this algorithm queued up. But pathologists still use it. Pathologists still want to use it. So imagine a world where this is even easier to do. It will become much more easier to use.
Speaker 3:So identifying metastatic foci, and the pathologist in the room might say why do you need an algorithm? I can just eyeball it. This is cancer. What if you have a few rare cells and you missed it? But the computer didn't miss it? These are type of things I call them rare event detection. Overall, right, it could be finding microorganisms and so on. Let's go beyond that, right. So today we can find metastatic cancer in lymph nodes. But we want to go beyond that right.
Speaker 3:Pathologists today do immunostains to figure out which cancer this is Tumor of unknown, primary, unknown origin we do several immunostains. I had a case last week. I had to do 20 immunostains and consult a metopathologist and soft tissue pathologist and even then you know guess how it was signed out High-grade malignancy, see comment. So this is an ongoing issue in diagnostic pathology and today we have algorithms which can predict for you, just like when you send it for next-gen sequencing. It can predict with greater than 95% certainty this is renal cell carcinoma. So this is an example of such a prediction model where the computer has predicted this is colorectal cancer. We have GI pathologist, dr Chen, in the audience and she will say why do you even need AI for this? I can look at this and say this is colorectal cancer. It has necrosis and dirty necrosis and all the features of colorectal cancer.
Speaker 3:But the point is there are algorithms out there and they are available. They will continue to evolve and get better and when you are ready in your journey, you will buy that algorithm and use it. But before you do that, we still have to solve the interoperability issues. We still have to integrate them and some sites have, some institutes have done better with these, others have not. So if you look at diagnostics for cancer overall, they can help you in a pre-sign-out process. They can help you during sign-out or they can help you post-sign-out. In pre-sign-out setting it could be a good screening tool. I showed you the data from hundreds of studies about sensitivity, right? So for prostate cancer specifically, similar studies have done and shown high sensitivity and high specificity and it's one of the most commonly exploited cancer for building algorithms. Like every company I talk to, they're building their own prostate cancer algorithm, pre-ordering IHC, right?
Speaker 3:Imagine a world where you're coming to work and the system automatically screens the cases and chooses the ones that you should do immunos on, and doesn't do it automatically but makes recommendation to you During sign out. It can find other features, like intraductal cancer of the prostate. It can help you with supervision, primary diagnosis or without supervision. It can also create automated reporting templates for you and then do a second review, like what about post sign out, right? So imagine a world where you are overworked pathologist, you've just booked a ticket to go to Las Vegas and it's six o'clock and you're trying to rush through your cases and you make a mistake. But you have the safety net, you have a gatekeeper, you have the AI assistant. They check your work and say wait a minute before you board the flight.
Speaker 3:Are you sure you want to call this cancer? And you'd look at it again and again and maybe you change your diagnosis. So some of the studies out there have looked at this specifically and there are cases where small foci or cancer were missed. Did it make a difference? Maybe not. Maybe there was cancer in other cores, but what if it was the only core and you missed it and you wish you had the system, or not? So how many of you if this was free right, all the vendors will give this to you for free how many of you will use it? And what if it wasn't free? You'll still use it.
Speaker 3:So again, we are also, as we build and use these algorithms, we're learning new information about these cancers. So, again, these tools are becoming, I would say, not in routine use, but they're getting very close to routine use. So you can see this work list as you are looking at your work list for the day and the cases have been flagged for you. So I would say we're very close to getting a routine use of these algorithms and, again, you can turn it on and off. If you're in a residency program, you can have the resident use it or not. You can use it as a way to assess their competency, right? You can make them review the case and then turn on the AI or make it so. Ai will only be turned on after five minutes of review. How many residents in the room You're ready to use AI, right?
Speaker 3:So this is an example of breast cancer, right, invasive lobular carcinoma. And again, the red areas are where the cancer is. You can go look at it more closely. You can also have algorithms which can distinguish the subtypes of breast cancer or subtypes of prostate cancer. What about finding mitosis? Who likes to count mitosis? Residents, you like to count, you know? I asked, I did this test. I asked three residents to count mitosis on the same slide and I got three different answers and I would say pathologists would also give the same different answers. Right? But you don't have to, right, today, if you have AI, you've implemented it. They will find the mitosis for you and let you check it or not. So again, this is an example of lymphoma classification, or finding abnormal white blood cells in a smear.
Speaker 3:These systems are already in place, like many of these systems are being used in the clinical lab. What about finding acid-fast bacilli? Who likes to do that? These are algorithms which are available. Some of the centers have implemented it. What about finding H pylori? So these systems are being built. They're going to become commercially available.
Speaker 3:There will come a time where you will have your own personalized dashboard of apps that you can download from the App Store, and you'll have to pay a subscription to it. Some of them will be free, some of them will be per click, but these are things which we didn't have when we were talking about whole slide imaging. So let's go beyond that. What else is coming? So we were talking about whole slide imaging, so let's go beyond that. What else is coming? Right? So I talked about the whole slide imaging. Is that the end of the journey? Is that the end of a glass slide? What about starting from the tissue? Right? So we're getting closer to radiology, and radiology is getting closer to pathology, so it's a spectrum. Right? So you have synthetic data, we have virtual staining, we have 3D pathology, integrated large language models. We have all these new tools which will become available in your work list soon, from pixels to diagnosis, to prognosis and predictions.
Speaker 3:Now we talked about the segmentation. But imagine a world where I want to go inside this segmented area and pick one or two cells and interrogate those cells. What if I want to look at this image in three dimensions, right? What about just finding similar things, right? Today you can take a picture on your phone and you're going to the supermarket and you take a picture and find similar products. It gives you the prices. What if you could do this in pathology?
Speaker 3:When I was a resident, how did I learn? I learned by opening textbooks. I was in the resident room and it was 7.55. The attending was going to come there at 8 o'clock. I didn't know what this was. I wanted to scribble something, so I flipped through pages and I put something down. At least I tried. But today this could be done electronically. It could be done using artificial intelligence, where, in this case, the query image was a brain ependymoma and the algorithm found the best matches which were similar to this and created. At least it gave you an indication of what you're dealing with. And you're going to see examples of these different things, like virtual staining. So if you walk around the exhibit area, there are vendors out there which have worked on it, right? So this, to me, will be going beyond the glass slide.
Speaker 3:So what about prostate biopsies which are unstained? On the top panel A, you have unstained prostate biopsies. In panel B you have H&Es which were generated in the lab. And in panel C you have prostate biopsies which were generated by virtual staining, and then you can actually see the areas where the cancer was. So what about staining, right? See the areas where the cancer was. So what about staining, right? So, as a prostate pathologist, we do prostate triple stain to look for basal cells. So here you see, on the right-hand side you have real HNE and on the left-hand side you have virtual HNE. But what is amazing to me is the ability to now predict where the stain might be and even predict intraductal carcinoma, which is a difficult diagnosis, in my opinion, you know, and there is a lot of controversy around it. So 3D pathology is coming. We have commercially available scanning systems now which can scan in 3D, right? So here you have a cancer prostate core and a benign prostate core.
Speaker 3:So what about helping you while you're signing out, right? So these are slides from Dr Singh, who's built this in Path Presenter the ability to create a chat with the image. So imagine a world where you're looking at this image. You start chatting with the chatbot what is this? And help. These large language models can help proofread your report, alert pathologists if you had missing information. You know this happens when you're in a hurry. You're rushing through a case. You might miss information, you might put something like a T4 where it was actually a T2. What if you had this way to proofread your reports and help with billing right? So imagine a world where you have this image where you can press a button and look at the features for the residents and the trainees. It can highlight the features in this image, but it can also suggest a diagnosis for you and suggest some IHCs for you and also try to help you find similar cases, create a differential diagnosis, take these images and put them into your tumor board pile and present it at the tumor board.
Speaker 3:So these type of tools are coming in many areas in pathology and again, we're not there yet today. But what I'm showing you today are prototypes of things that are coming in the pipeline. So we've gone beyond the glass slide. We have now demonstrated that we can make a good diagnosis on digital images. We have demonstrated that there are algorithms today which can be used clinically, and I've shown you examples of workflows that are coming soon.
Speaker 3:So I just want to also separate artificial intelligence and real intelligence, right? So when you think about artificial intelligence, it's very task-oriented. Right? So when you think about artificial intelligence, it's very task-oriented. Find me this feature how many nuclei are positive? Biomarker quantification, lymph node met? I showed you all these examples, but they're very task-oriented. Each algorithm is composed of small steps, but real intelligence is goal-oriented Many algorithms per task. So, as a pathologist, if you look at your journey from residency, from medical school, to where you are as an experienced pathologist, it's experience-based, it's context-based, it's knowledge-based. So we're not there today where we can say AI is equal to real intelligence, right? So that's a journey, just like digital pathology is a journey.
Speaker 3:So this is an example of a case of prostate cancer where, on one, one of the cores, I have clearly established prostate cancer, right here and then this is which looks like prostate cancer, and I actually signed it out as prostate cancer, and that's where real intelligence comes in. So I signed out this case, I released it into the patient's medical record, but something bothered me when I reached home. You know, that night. I just had a thought that this, maybe this is rectal cancer. Maybe we should think about it. So I dug through the notes. I found a note from one of the primary care visits where this patient has rectal bleeding but refused endoscopy because it was too expensive. So I went in, I ordered some stains and it turned out to be colon cancer. So I had to admit I was wrong. I amended my report, I called the urologist and the patient actually got treated for rectal cancer and prostate cancer. So I think that's where we need to be and we might get there in 20, 30 years, but we have significant advances in the field even today. So, putting it all together, everybody went to Magic Kingdom. I was there last night Amazing fireworks, the best fireworks. I mean, I went to Disney with my kids many years ago, but I went now Amazing, so I recommend it highly. I don't have any stocks in Disney or anything.
Speaker 3:So, in conclusions, how will digital pathology and AI help pathologists? Right? So we talked about the decline in number of pathologists, increasing workloads, fewer trainees going into pathology, and clearly what I've shown you today can help some of those issues. Help us with sharing cases, help us with connecting subspecialists together, connecting spaces together. We have increasing workloads, so are we ready for a digital disruption? And then augmenting your diagnosis, checking your work, do some of your work and share the work with others right, so you have to tame your own digital pathology chasm, right.
Speaker 3:Each one of you is here in this conference because you want to learn more about it. If you've already bought the scanner, you want to learn about AI. If you've already implemented AI, you want to learn about 3D pathology. But it's a journey, right, so all of you have to solve it on your own. I'm not going to you know. I'm just showing you where we were 20 years ago, where are we today and where are we going 20 years from now. So maybe you will have a workstation like this in your office where you will customize it. You'll buy your own apps. You will create your own chasm building, right, so you will use AI to assist you to augment your diagnosis. And maybe autonomous drive, right.
Speaker 3:Yesterday, my friend drove me from the fireworks back and the car was driving itself and he left the steering wheel and I was super scared and it was pretty crowded. It was Sunday night, it was rush hour. I said, no, I'm not ready for this. So take over the steering, please. I have a talk to give at 7.30. So he didn't listen to me and the car drove itself for 10 minutes and it did fine, right. But you have to build that trust. You have to build that trust with AI, right. So where are we going? Right?
Speaker 3:The journey from glass to digital to prediction will continue for all of us. We are today. We can improve our analysis. What will come is next, we'll be improving your diagnosis. Maybe we will have more integration. We will solve the interoperability issues, regulatory issues, reimbursement issues, but in the future, this will really be clinical decision-making exercise and integrating multiple types of data, and we're probably this will be the era of AI-based precision medicine in its true sense, and that's probably in pathology. Ai could probably diagnose easy cases independently, just like the Tesla was driving autonomously yesterday, but I don't know how many pathologists feel comfortable about that riding the Tesla of pathology. So with this I'm going to conclude. It's been an amazing journey and I think we're going to.
Speaker 3:I actually encourage you to talk amongst yourselves. Go to the exhibit hall, look at all these different products out there, and there is something for everyone. You know you might be just buying your first baby scanner. You might be just getting that one big check from your administration and you're ready to spend it. This is the place to do it. This is the Disney world of digital pathology. So with this, I'm going to end with a picture of football again, which I'm excited. Next weekend I'm going to be at the game, and so I'm going to stop and if you have any questions, I'll be glad to answer it. I'm going to be around for the rest of the conference, so I hope to interact with many of you. You and I want to thank Apreedia for inviting me here as a speaker, and looks like a full house. I'm sure it's because of the breakfast. That was really good.
Speaker 4:Thank you all thank you so much, dr Parwani, for your lecture and I think it was amazing because it gave this like from the very beginning, when scanning was a problem, to now like doing 3D pathology and actually everybody who is in this room and who comes to this conference, they can be at any like single point of what you described. So let's start with the beginning, and you were talking about the chasm. So everybody has their own problem, their own pain point to solve. But let's start with the scanners. What scanners do you?
Speaker 3:have so. So we have variety of scanners. Uh, we have philips, we have aperio uh laika, we have hamamatsu and we just got the predia. Which one did you get the 250 the fluorescent. Yeah, yeah, so we're using it for our kidney biopsies now is that the one that has the polarization? Option as well.
Speaker 4:I remember in our podcast you were saying why they are not adopting the renal pathologist, and now there is a tool for them.
Speaker 3:Yes, I think that to me is an exciting part, where you can actually see some of the users who are turned off by digital if they cannot do A, b and C. Today we have the capability of providing those options with different types of scanners. So I think one scanner may not address all the needs of a big academic center but a smaller lab can actually do with one or two scanner types. But there are so many complexities in pathology and I'm glad many of the vendors out there, including Apreedia, leica, are actually solving those problems individually or incorporating them into your dashboard, into their system. You know, just like when you go to Best Buy to buy the next TV, you have all these bells and whistles, but they specifically build those based on feedback.
Speaker 3:You know, today I cannot do this. Can you build this? So I think digital pathology scanner market is also evolving in that direction. To me, it's like multiple trains have left the station and everyone has their own train and they have their own next stop. The next stop might be I want to do frozen. The next stop might be I want to use image analysis. I want to do large language models. I want to go directly from the tissue to an image, you know.
Speaker 3:so, all these trains, individual trains, are users and they have expectations and they have end and they have an endpoint. Not an endpoint but a stop on the way. And they want to get off the train at that. Stop, do something and then get on the train again.
Speaker 4:Figure out what the right course is.
Speaker 3:So I always think it's fascinating to me to come to a conference like this, where you have different types of users, not just pathologists, but also technicians, also students, technologists, it vendors lab managers and they work together to solve complex problems. Well, this is what I love about this.
Speaker 4:I like it about digital pathology because usually in medicine you don't have a team with so many different expertises. You mostly are with medical professionals and it's driven by the medical professionals, and here you have technology specialists, you have operations, you have, obviously pathologists, you have the administrators and I love it. It's super interactive.
Speaker 3:Yeah, no, I think you're exactly right. You have the administrators who write the checks, you have the end users, you have trainees who are going to be the future of pathology. So they're all coming together for finding their own journey, finding their own discovery. You know why digital pathology? Why now? Why me? And I think that is an important thing, which is important for your listeners also to know.
Speaker 4:And we just heard from one of the people who was asking questions like OK, now we got the green light, how do we bring everybody on board? Correct, Because I think people are. Well, it's with any change management you're focused first on getting the green light and going somewhere, and then it turns out you have to bring everybody else with you. How do you do that? That's the next step, step next stop on this journey.
Speaker 3:Yeah, I think, I think you have to take baby steps. You have to again as a visionary. You have to look at the vision of where you want to reach right and that's where you're here, so, but then the vision could be. I want to do these steps and this will require these changes.
Speaker 3:A good leader is also a good change manager you know if you're not, you're not going to be a good leader if you cannot manage change. The change can occur at people level. The change could be at the technology level, it could be financial, it could be regulatory, but collectively you have to orchestrate all those changes to see and execute your vision. You have to hire the right people, the right team and keep them engaged, keep them excited in this journey so what is your chasm right now?
Speaker 4:what are you guys working?
Speaker 3:yeah. So our chasm is, like I mentioned. There are different taint preparations, light preparations, gosh frozen all those. How do we bring all this together in one seamless way? So the chasm that we're trying to solve is integration with all the AI tools. Do we need to launch multiple viewers? Do we need to create technologies which are not compatible and try to bring them together? So that is the chasm we're trying to do. You know we can create diagnostic quality images. We know we can use them. I know we can make diagnosis on them, but I think the next gap is can I create a dashboard where I only have three AI tools but they could be launched from one viewer, and so on?
Speaker 4:For the ease of use for the pathologist, because that's also a step in adoption journey. When it's not seamless, people were not already convinced and not willing to troubleshoot. That's gonna be a showstopper for them.
Speaker 3:Yes.
Speaker 4:I love your stories. Thank you so much for this fantastic presentation.