Aleks: [00:00:00] What if your entire pathology workflow? Cytology, anatomic pathology, all the way up to the final report could live within one platform. In this episode, I’m joined by Doctor Tiffany Chen and Ben Cahoon from Techcyte to talk about Fusion. They’ve integrated the digital pathology platform, combining diagnostics, AI, and reporting into a seamless experience. We’ll cover AI and cytology, standards-based interoperability, and what it really takes to bring digital pathology into your daily pathology practice.  Let’s dive into it.

Welcome, my Digital Pathology Trailblazers. I have listened to Tiffany’s webinar, and Doctor Chen gave a webinar on a new software from Tech Cyte. And Ben was my first, one of my first guests on the podcast. When you guys were starting, I don’t know if you were starting out, if you’re already ongoing, but I want to start with you.

Let’s, Tiffany tell the listeners, [00:01:00] the digital pathology trailblazers about you, about your background, how you joined Tech Cyte, and what’s your role in the company?

Tiffany: Hi, everyone. My name is Tiffany Chen. I’m the chief medical officer here at Tech Cyte. I’m a board-certified anatomic pathologist. I did my medical school training at the Mayo Clinic and then did my residency, pathology residency training at the Brigham and Women’s Hospital.

And then after that, I did a, a formal NIH postdoc in computational pathology. I fell in love with computational and digital pathology and decided to go into research. And then I moved into biopharma, and shortly after that, maybe two years after that, I moved to Techcyte. So I joined Techcyte, almost a year. It’s been almost a year now.

Aleks: So yeah, we’ve seen each other, like, that’s the third conference…

Tiffany: The third time we’ve seen each other. Yeah. Which is great. It’s always great to see you.

Aleks: Exactly. Ben how about you?

Ben: I went to school. [00:02:00] I’m a computer scientist. That I had the opportunity to work at Intel early on in my career. And so they basically paid for me to go back and get my MBA. And then after that, I did….

Aleks: Why not?

Ben: Right? Right, right. So after that, I had basically been running and building companies and, mostly software.

I love software, and this, Tech Cyte has been amazing because we’ve been able to impact not only the lives of humans and the people who are getting the tests, but also of pets and the environment. So we have 2 or 3 divisions of the company.

Aleks: Yeah. Let’s go. Let’s give a quick intro to what Techcyte does.

Ben: Okay. So the Techcyte has a digital platform, a SaaS based platform that combines workflow plus AI, diagnostic testing, and pathology.

So we started off with a mold test. So I can quantify about 160 different types of mold particulate and…

Aleks: And I remember how [00:03:00] surprised I was with that when we first talked about the is it’s a big deal in the industry.

Ben: Yeah. So it’s growing really well it’s it’s a great business. It’s doubling every year. So we’re really happy with that business.

So, to home inspectors and industrial hygienists. And then we partnered with Zoetis to build out their digital diagnostics platform on their Grundium scanner. So that has been growing very, very nicely. We release everything from blood, urine, parasitology, digital cytology. And that’s now all over the world. And then, we have been focused for many years on clinical pathology, which is we’ve defined loosely as liquids and cells.

So like we do pap smears, which is officially part of anatomic pathology. But we’ve really left tissue roll, for others. Until today we announced our Fusion AP product, which, is a digital pathology platform for both surgical pathology [00:04:00] and use cases such as, the use cases such as frozen section and education and pharma, etc..

Aleks: Amazing. Following on that, on the mission, what is Fusion, and how does it relate?

Combine what does it do with the work that you did previously? Because there was a lot of things that are done in Techcyte and now and my impression is Fusion is fuzing it all is binding it. But I would love to hear from both of you.

Ben: Yeah. Thank you. So we Fusion is built upon the same base that we built our clinical pathology platform on.

So it includes, everything. We did add a lot. So it includes both a workplace and a viewer. And AI all included, for it, but specifically designed for anatomic pathology versus clinical pathology. And, if you look at anatomic pathology workflows, they are much more complicated. And so we’ve been, we’ve been very, very busy coding that up for about a year [00:05:00] and a quarter.

Aleks: What does it mean for the pathologists?  And basically for healthcare professionals, everybody involved in the pathology workflow, and for the patients.

Tiffany: Yeah. Like Ben had mentioned, pathology workflows are quite complex, and they can be quite fragmented as well. So, you have lab techs, you have histotechs, scan techs. Now with digital pathology we have our pathologists admins, trainees.

So you can imagine everyone needs to work together to really further patient care. So for Fusion we want to make this experience just way more pleasant and, and delightful for these users. And not just the pathologists, but everyone who’s involved in this part of patient care and for pathologists, their day-to-day, just speaking as a pathologist, when you’re going through your patient cases, you’re going through different systems as well.

So you may be living in the LIS and then in the EHR, looking at prior radiology, trying to connect all these different pieces together [00:06:00] to help your patient get the best diagnosis. And for Fusion, we just want to be able to let pathologists focus in one place and bring all the data points to them, to let them really evaluate the case and get the best possible diagnosis they can, and to generate these diagnoses with confidence as well.

Ben: When you think about Fusion, you were right. We’re bringing together. You’ll be the first. It’s all a digital pathology platform to bring together both clinical pathology, anatomic pathology, but also all of the systems and the people together. One of the key points that we learned in clinical pathology if you need to be able to infuse AI in that workflow, if you don’t infuse AI into the workflow, you won’t see the efficiency, the accuracy gains that we’ve seen in other areas.

Aleks: So that’s something I saw at Path Visions last October. That was the pain point that pathologists have, that they have to open this window and that window. And, and, you know, this pain point [00:07:00] is being addressed from several angles, including large language models. But the the main thing that they were suffering from, like, just give it to me in one place so that I can do my job.

And it sounds like that was your guiding star as well for them. We make it easy for the pathologists. We have clinical, anatomic. But what does that mean other than just not opening multiple windows? What are the efficiency gains for somebody who’s going to start using this? Both on the organizational level and on the user level.

And you already mentioned, okay, we have the option to, infuse AI. Anything else? Like I want to hear the tangible things that people will gain when they start working that way. I think it’s for organizations, pathologist patients.

Ben: If you look at, pathology labs, what’s what’s the thing that [00:08:00] they hate the worst that’s dealing with IT.

Right. And being able to get the time and the effort to, to really integrate the systems with a team. So we, we make that very, very easy by using an open standard-based platform. So standards like Fire, Dicom, Intel 7 are all infusing the platform so that it makes it easy on IT to integrate these systems in from a purchasing perspective.

Right. You can go to one vendor, to do the security audit and other regulatory issues that you’ve got to face when you’re implementing a part like this. So we’re trying to make it easier for both purchasing IT and C-suite way, to make it an easier process there.

Tiffany: For the user, at least for the pathologist. I think the first thing we have to do is really digitize their workflow before we can start enhancing it using AI.

And we’ve seen a lot of developments within AI in the last few years. So, really advanced AI for diagnostics. But there’s, there hasn’t been a clear path on how we can actually deploy that within the clinical workflow [00:09:00] to get those efficiency gains that have been promised. So for us, we have an AI marketplace that we plan to support as many AI, vendors as possible and and including our own Techcyte AI or any institutionally developed AI as well.

And we believe that by hosting it directly within our system, we can pull it into the clinical workflow. Pathologists can actually use it in their day to day to help them prioritize their cases, triage, assist them with diagnostics, and that will really just, finally deliver on what AI and digitization can do to the field of pathology.

Aleks: I think that’s so important because there are these like, and we’re going back to the concept of Fusion, the concept of merging this, because the possibilities are out there.

But the implementation, the applications, not yet because it’s just so much hassle to do it.  And you mentioned standard based platform that is so important. Are It your friends [00:10:00] now.

Ben: We hope so.

Aleks: Because I just had the conversation yesterday at the breakfast. That. Oh, there should be somebody consulting, those institutions, how to integrate it in, in AI.

And they said, well, I don’t know if you really can do it because everybody has a different architecture, everybody has a different database. There are different like homegrown systems for something that has to be integrated. So I think this is actually the role of the vendors to guide the institution’s pathologist, whoever’s using it. So, you guys are guiding them closely and working with I.T., I assume.

Ben: Yeah, I’ll give you an example of that here at the show.

We’ve integrated both Aiosyn and Aiforia into the platform. And the annotations are coming back through the kind of annotation standard. So that’s, a pretty cool development.

Aleks: In the way I’m so excited because annotations are always like, okay, DICOM image format and all that stuff, more or less, [00:11:00] interchangeable all across platforms. You can ingest stuff from different scanners, but I feel super strongly about annotations because there are so many pathologists working hours going into that, and then it just sinks into the projects and you can never reuse it.

So I think you’re the first person who actually said, oh, we can exchange annotations. Thank you. Thank you for that. Let’s talk about the collaboration you said you, you integrated the with Aiosyn and Aiforia. What are you collaborating on. What did you integrate and how is it going to help.

Ben: So let’s start with Aiosyn, it is a pretty cool that they now have CIVDR cleared algorithms.

And basically what we did there was two key ones initially. First ones like you see, so being able to if you don’t have QC integrated into your scanner, then like many scanners don’t have that today. They’ve, they created this like you see algorithm and we’ve integrated into our, our manual like QC tool [00:12:00] that we built.

So if you, if you don’t have AI, if you, if you’re just doing this manually, we’ve got a tool to help ease that. And then we’ve integrated into the, the, their AI so it can automatically find blurry regions. It folds in, and other issues with the slides. And then we’ve also integrated their other, algorithm, like, what is a kidney there?

Tiffany: Oh yes. They’re glomeruli like their kidney algorithm. Algorithm, their mitosis counting algorithm as well.

Ben: Correct. And then with Aiforia, we’ve been working closely with Mayo Clinic and they’ve used Aiforia extensively within their organization. So they wanted to be able to bring those algorithms that they built onto the platform. So we can bring in both Aiforia externally created algorithms, as well as if an organization use that for you to create new algorithms.

Aleks: I think it’s it’s kind of, I would say it’s a superpower of Fusion because you guys don’t have the capabilities to self develop algorithms, right?

Ben: No, we do.

Aleks: You do as well. So you can. So, let’s talk about that as well. [00:13:00]

Ben: So we yeah we’ve been developing algorithms, boy, back since 2017 when we just after we had started.

Aleks: But, is it your team or is it the users?

Ben: No no it’s our team.

Aleks: Exactly.

Ben: Yeah. So our team okay. Yeah. So Aiforia allows other users to create algorithms. We do we create algorithms ourselves. And then those algorithms can be in the platform. For example, Tiffany and, and the Mayo Clinic team have been working on some dermatology algorithms to rotate the epidermis to the top and count mitosis and look for inflammation.

Aleks: Yeah. So so that’s exactly what they wanted to emphasize that there are these platforms. And you know, Aiforia is your partner. There are others where they’re researchers. And Mayo Clinic obviously has a strong research group. And I interviewed, several of their researchers, Doctor Rish Pai on a podcast as well. And he was working with Aiforia.

And that’s that’s superb big effort that they have. And then there are the platforms like the one of yours where you come [00:14:00] in, where you collaborate more with an institution to build something that is deployable, at an institution. So how do you merge these two approaches? And it looks like you just merge this. So so how can they, like, just keep, so they develop their stuff and then they can deploy in Fusion.

Tiffany: Like an engineer…

Ben: Basically you create an algorithm in Aiforia you can easily deploy it out right, with in Fusion or use one of our algorithms or other partners like that. Like, Aiosyn.

Aleks: And you don’t have to double-annotate and nothing like you just did the work once, and then it goes into another system. This is a superpower. I think it’s the first time.

And you know, the superpower only lasts for so long as everybody sees it, and they want to do it as well because it’s a good development. But you guys are the first company where I see this, on-demand image analysis algorithms that many of them never make it into [00:15:00] a tool that could be deployable in through an organization, and you have a group that works on that.

And then they ask, whatever makes it, makes the cut can be deployed. So that’s a tool. Tiffany, this one is for you because it’s about AI for pathologists. Other than workflow. And I specifically would like to know about the cytology algorithms that you guys have. How does it change the pathologists life.  And you already mentioned pieces of it.

But let’s talk a little bit in depth. How is it gonna be different for the pathologists in their job now with the AI. And feel free to like do tell me specifically what you guys have and how it’s going to work.

Tiffany: Okay, great. Yes. So we’ve been really passionate about actually making AI deployable within clinical workflows. And I think a great example of that is our Sure View product, which is our Techcyte cytology product that we’ve, I guess launched a few a year ago. [00:16:00]

Ben: A year ago. 

Tiffany: Yes, a year ago.

Aleks: The one Core Plus is using?

Ben: Yes, Core Plus is, BD distributes it.

Tiffany: Yes, yes.

Aleks: So I’ve heard about that. That’s why I ask the question. So yeah. So Sure View is, is an AI tool that helps cytotechnologists identify and triage their cases and flag, regions of interest. So this helps build confidence and really empower them to be able to go through their cases, with the software to be able to identify like these regions.

And, you know, with cytology, we’ve been dealing with a lot of high volumes and, with a lot of pathology, you see, there’s high, intra, observer variability. And it really depends on like levels of experience, especially for cytology, where the morphological changes can be quite subtle. So with this type of software, we, we want to empower them and make them feel more confident as they go through their cases that they aren’t missing cells.

And this is really driven down [00:17:00] false negative rates. And, has just improved their day to day life. They feel like they can do not only more cases but do them well. And for pathologists out of pathologists, it’s we have great happy cytotechnologists who are able to help you screen that helps your life significantly as well. So we’ve seen, just really great feedback across the board from Cytology Labs have been using our product.

Aleks: So is this now incorporated into the current workflows. So so this is radiology has more of those screening tests. This cytology test, for cervical cytology is one of the few that we have that they’re more screening than diagnostic. And it was always okay. AI is a perfect like help for a screening test because the current workflow and, you know, I’m going to ask you to elaborate on this.

The current workflow is the cytotechnologist screens and then show stuff to the pathologists. Right. Can you compare and contrast the, [00:18:00] classical workflow and the enhanced workflow?

Tiffany: Yeah, the cytology is really unique within pathology. Like you mentioned. It’s one of the few fields that have the screening mechanism built in. And cytology is actually, I would say, one of the first subspecialties within pathology that has moved towards AI.

So this screening process has been around for many, many years within cytology. And now we’re incorporating more things like AI assistance into it. So in the past we have cytotechnologists still screening all the cases and then they’ll flag specific diagnoses. So if it’s a positive diagnosis for any, type of cervical lesion, that of course goes to the pathologists, but they’re also borderline cases or reactive cases.

That can be really tricky. So those are flagged as well. And a lot of these are according to, you know, obviously guidelines but also by institution. They have different, limitation, limits to, when [00:19:00] they will for the cases of pathologists. And in addition, they’re things like organisms, that also need a pathologist to review. So as a cytotechnologist, you not only have many cases, but you have all these rules of like when to send these cases to pathologists and all these different diagnoses that you have to make.

And you can imagine that that can be quite a lot for a cytotech. And the cyto volume…

Aleks: You guys have that integrated.

Tiffany: Yes. So our product is for like the cytotechnologists to really improve their day to day workflow and help them triage those cases. We have reporting built in. And if the reporting, shows a specific diagnosis that gets, like elevator leveled up to the pathologists, the case will directly go to the pathologist queue.

So it saves them that extra time of being able to remember, like what cases they need to move forward. And it’s all in like one place for them.

Aleks: This is amazing. That’s like the workflow efficiency [00:20:00] on steroids, I would say, especially for this use case because like you say, it’s high throughput and lots of negatives, but then you’re never 100% certain, always negative.

So if you have an AI assistant that changes the game for sure.

Tiffany: And it requires, I would say, a lot of experience and skill to train your eye to be able to identify sometimes single cells within a slide with thousands of cells and, there’s you know cytotechnologist. I come from all backgrounds and all levels of experience, and we want to be able to elevate and empower all of them, regardless of their experience, so they feel confident as they review.

Aleks: I think when, this is often like this, AI assistance is often, especially by the, less tech savvy, a group of pathologists or those who are maybe late adopters.

This is like this. Oh, are we even gonna be, thinking, on our own? Are we going to be able to [00:21:00] recognize it, or are we gonna blindly trust AI? I think it’s the other way around, because you get so many examples shown so fast. And this is how you gain the pathology expertise. When you look at it, it’s pattern recognition, in your brain and in your eyes.

So the more you see of it and the better. And then you can have you have it flagged, you have it like labeled on the image. That’s another story. Like define these things within the image. So the AI helps to localize. And then you have the pathologist giving you feedback okay. Was it really something that should be flagged.

And it’s not just for the algorithm. It’s also for the person who decided to click whichever button and send it to the. So I think that, learning and training like to say they came from many different backgrounds, is accelerated, but just using this tool. So that’s another advantage. I think in general you just have to incorporate that in one platform.

Fusion also has the bacteriology right. [00:22:00] The gram staining. How do you work with microbiologists and how is that integrated. Because that’s even outside the pathologists. The pathology department in the hospital. Right. Are you…

Tiffany: In the US. So microbiology will fit under clinical pathology departments. And I think globally, microbiology I think sits in its own department.

But at least from what we’ve seen so far in the US, we it’s a, it’s a core part of the clinical pathology workflow and similar to how it is for cytology, within microbiology, you have, lab techs who are triaging and, and reviewing and screening cases. That can be quite tedious, as you can imagine, trying to identify a little bacteria on your slide.

And it’s not only tedious, it’s like very strenuous on your eyes as well. So we built, our, bacteriology product that allows the text to be able to, to screen their cases faster with more confidence as well. When it comes to, [00:23:00] being able to identify things from gram-positive to gram-negative, cocci or rods, as well as other things like, like white blood cells and epithelial cells too.

So, Ben you wan tto ass?

Ben: Yeah. So this project was developed with Mayo Clinic. It was a very tight collaboration. Their expertise in their data was all used and a workflow with AI. And I basically to think that whole process more efficient, more accurate. And that was also, that product was developed to work with manual smears, for pan smears, as opposed PPE.

So, whatever your, whatever TLA solution or manual method you’re using, you can use that to scan the slides, get it up to the cloud, analyze it, and then you’re presented with the results so you can compare the culture results to the to the ground state.

Aleks: You mentioned Mayo Clinic a few times. I think being able to develop a product in such environments, is a game changer [00:24:00] because you basically have it pressure tested from all sides.

So, that’s super cool that you get the that you had the opportunity to work with them. And have, how many pathologists worked with you on that project, all of that?

Tiffany: A lot, a lot of, pathologists. But we also incorporated in a lot of the text, too. So, so we’re again focused on building those products that can that can help multiple different lab users and stakeholders, not just the pathologists.

We’ve seen with microbiology, like the, the texts, they’re carrying a lot of these, the volume and then the, the screening and all of that. So we, we incorporated a lot of their input and feedback into the development of this product.

Aleks: Amazing and very comprehensive. And I would say basically compatible with the mission of a healthcare institution.

Right? You can develop a nice tool for one specialty. Oh, and that’s something [00:25:00] I heard, yesterday at lunch, I was talking about, GU, Genital Urinary Specialist. And she says, oh, I don’t do digital pathology, but my colleagues, pulmonary cases, they all get scanned. And these cases, they all get scanned. I’m like, so in the same institution, one pathologist because zero experience with digital pathology and the other one is used to working fully digital.

So and that’s not, an isolated case.

Tiffany: No, no. That actually is seen across many institutions. It’s just it takes a lot to move everyone on to digital. So they tend to do it quite piecemeal with certain subspecialties. Their workflow may work better digitally or there’s just less resistance there to.

Aleks: That’s a very important one, to do that first one, less resistance.

Ben: We actually started down the whole microbiology path with parasitology, and we…

Aleks: Remember the solution for scanning the fecal sample…

Ben: Yeah it’s very difficult. So we’ve been introduction to air pieces since [00:26:00] 2019. So hundreds of thousands of fecals have gone through there. But then we just released this year, as solution for wet mount fecal. So in the US you go track from and wet mount to get the best solution.

And now we’ve got a full solution for parasitology as well.

Aleks: So we have microbiology, AP cytology, parasitology. It’s like everything you can look at the fits on the slide. Yeah. Anything on a cellular level, let’s call it cellular level fits into Fusion. That’s super cool as well. We already talked a little bit about the partnership with Mayo Clinic, but other than this helping you develop a robust product for an institution, how did it influence your company and in general, like the way you work or what?

Did it change anything? Or was it just like a great partnership and we have a good product that people can use? What would you say about that?

Ben: Yeah. So before our relationship [00:27:00] with Mayo Clinic, we were really forced to go and get data from all over the world. So, a lab in Mexico, a lab in Luxembourg. We worked very closely with the AUP group as well.

And the relationship with Mayo basically solidified our ability to get amazing data, work with amazing experts to create workflow and AI. Then we can then figure out the best way to get that out to the world. So the best example of that is probably initially, cervical cytology. We developed it with Mayo, and now BD is distributing that solution worldwide.

And that’s that’s sort of the models develop things with Mayo and get it out to the world.

Aleks: Because it does not just stay within this like limited amount of institutions that are digitally enabled. You kind of leverage commercial expertise in the commercial power of your distributors. You did the [00:28:00] similar thing with so at this. So I love that this goes to the world and the increased, adoption and advancement of digital pathology, because, you know, developing a nice product is one thing, but then getting it out there for people to use, not on the, on the logistics organizational level, but also defending, like you mentioned, change management level, comfort and comfort level of users, not only pathologists, but the whole care team. Tiffany, for both of you. But, I’m going to start with you because I heard you talking about this at the webinar, and that was audience questions. How, does Fusion integrate with the LIS? Is there electronic health records and all the systems, and which systems does integrate with what would be the path for somebody if they wanted to use it to be compliant and to be integrated?

Tiffany: Yeah. So for Fusion, we want we built it to be flexible. [00:29:00] Depending on your labs needs. So some labs, just depending on what LIS they have or what EHR their hospital or their clinic uses. And it really depends on, those factors as well. But for us, like Ben mentioned, we are standards-based. We want to integrate with whoever is willing to integrate with us.

And for us…

Aleks: There are a list of the standards that you guys are compatible with or based on.

Ben: Yeah, Fire, HL7, DICOM, DICOM web, you know, and then you go a level down from there. But those are the key, pathology perspective.

Aleks: So basically if somebody wanted to know, they reach out to you and you have the list and all the specs and everything that it integrates with, and if that’s what they want to integrate, you’re good to go and you can start working together.

Tiffany: And that that will be for us to work with their vendors and their vendors. Also our standards based and can can communicate with us. We are able to, you know, integrate directly with them. [00:30:00] And how we design Fusion is if someone wants a LIS driven workflow, they can still do that with Fusion. They want a PACS-driven workflow.

They can do that with Fusion. So it’s it’s built to be flexible. And we realize that, you know, labs functions so differently. And the lab infrastructure, the IT infrastructure varies so differently. And systems that they use are also so different. So for us, we believe we can stick with the standards and, and be willing and open to develop like, bi-directional integrations, we can really tap into this like optimal experience with Fusion.

But, we will support many different workflows depending on how we can integrate there.

Aleks: So what’s next? This is obviously a milestone that I’ve been hearing about since last USCAP, right, when we met briefly. Yeah. So what’s next?

Ben: So now that we’ve got the, you know, [00:33][1:00] the basics of a platform in place where you can connect to the EHR, the LIS, the VNA, you know, for grossing images, etc.. Now that we’ve got that basis, we will be adding very specific workflow and AI for each of the subspecialties. So if you look at Derm, Breast, prostate, GI, infectious disease right there, there are lots of opportunities in each one of those subspecialties has unique problems and challenges that they have to do, whether it’s carrying an estimation or like with Derm, rotating the epidermis on top, all those little things make a difference.

Aleks: I’ll be the pathologist, and all the workflows that we talk about.

Ben: And then taking. So if you think of the workflow of the AI that will drive efficiencies into reporting. So I think Fusion we’re just at the very beginning of what reporting will look like in ordering stains to then eventually get to that report. But the tight integration between these, [00:32:00]0] the…

Tiffany: LIS

Ben: Well no, the viewer and the reporting…

Tiffany: Oh, the view and the report.

Ben: Yes, will will be like, for example, synoptic reports. We actually have integrated all of CAP’s reporting templates, reports into the system today. So you don’t have to go to another vendor to get that. You can get it right within Fusion and then export that out to the LIS.

TIffany: And it’s really cool with, how we’ve set it up with reporting right now, where you can have smart reporting, and you have fields that require measurements, you can drop in an existing annotation, or you can make an annotation a measurement like right on the spot.

And this will fit directly within the report. And we believe like this will just make it easier. When you’re doing reporting, you don’t have to, you know, scribble down a number and then remember it and copy it down somewhere else, or any of that manual entry. And that will just make the reporting experience just much more pleasant.

And synoptic reporting can be a long and tedious process. You have to bring in measurements from the gross. [00:33:00] You have to remember, like margins and distances that are spanning different slides. And we want to make sure that, for the pathologist who’s doing the synoptic reporting, they can do it very efficiently. So we’re really excited about, being able to incorporate that in.

Aleks: Are you looking into large language modeling enhancements and Agentic AI and you kind of like have an agenda workflow already when you were mentioning how it goes, how it gets flagged for the cytology, and then what’s the decision to elevate it to the pathologists?

So that’s when I thought, okay, that’s that’s an Agentic workflow. Any thoughts on that?

Ben: Yeah. There will be multiple areas to integrate Agentic AI. Prioritization at the case that the workflows level, taking that EHR data that we now have access to and surprising that and then as well as the whole opportunity to order stains and then integrate and generate reports. [00:34:00]

So there’s there are multiple places where we see Agentic AI getting infused into it. Fusion.

Aleks: Thank you so much. I will keep visiting. We’re going to do a demo. So if you’re listening to this on the audio, go to YouTube and check the video about the Fusion demo. Thank you so much for joining me. And I’m super excited.

And I’ve been waiting to do this podcast for a year. Guys…

Ben: Thank you.

Tiffany: Thank you so much.

Aleks: If you’re into your USCAP, USCAP and come to booth number…

Tiffany: 920

Aleks: 920, 920. Were at booth 920. So visit us, and if not, USCAP the next conference. What’s your next conference, guys?

Ben: We’ll actually be in Europe in a couple of weeks in ESCMID, it is for the big microbiology show. And then the big one this summer is ASM. And then there’s there’s lots of other shows.

Aleks: And look for Techcyte and check out Fusion. Thank you so much.