Ian Maurer

Most AI Agents Are Toys. These Ones Treat Cancer

Dev in the Details #11 · May 6, 2026 · agents

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Key takeaways

Description

Ian Maurer is CTO of GenomOncology. The company is 14 years old, based in Cleveland, and builds precision-oncology software. The software helps molecular pathologists turn raw sequencing data into reports and treatment recommendations. It runs on-prem and integrates with hospital systems.

Ian walks through the pre-LLM NLP years. From 2015 to 2020 the team combined OCR, regexes, BERT-based models, and dictionary matching against UMLS, then added human review to catch the mistakes. Vision-capable LLMs now read even a barely legible scanned pathology report and return clean markdown for a tiny cost. Ian calls document understanding a solved problem and says the team can now focus on genomic insights.

Ian explains BioMCP, his open-source connector to public biomedical data sources such as PubMed, ClinicalTrials.gov, and variant databases. He gives it away because code and public information carry no business value on their own. He defines an agent as an LLM in a loop with a goal, a focused set of tools, and skills. A task with known steps belongs in a workflow instead.

Dev and Ian close on labor displacement. Ian points to Geoffrey Hinton's radiology prediction. Automating one narrow task made radiology cheaper, and demand for radiologists went up. Ian expects the same pattern in medical coding and software engineering.

Transcript

Introductions

Dev: Hi everyone and welcome back to Devin the Details, the show where we try to cut through the AI hype and focus on what it takes to build and deploy intelligent systems in the real world. Today’s episode is a little bit different and I’m really looking forward to it. When we talk about agents, LLMs and enterprise workflows on this show, uh what usually happens is, you know, we work focus on how different enterprises and organizations are thinking about their production deployments of AI, the security and risk profiles around them. But today we’re going to be talking about what happens when you take those same ideas and apply them to something that’s even higher stakes, like cancer treatment.

I’m joined today by Ian Maurer, CTO of GenomOncology. Ian has over 25 years of experience building complex systems and today he’s leading a team working at the intersection of AI, genomics and real-time clinical decision support. What stood out to me though is uh about Ian’s work is just how practical it is. It’s not theoretical AI, we’re talking about systems that pull together drugs, clinical trials, safety data, research and is looking to be able to give clinicians a full picture and the ability to actually scale this out as well via AI agents.

So in this episode, we’re going to dig into what it takes to build agentic systems in health care and how reliability and trust also operate on higher stakes domains and what the future of AI looks like when it’s not just optimizing a workflow but actually now in the loop in a more sensitive field like health care. Ian, really excited to have you here. Thanks so much for joining us today.

Ian: Yeah, thanks for having me.

Dev: Awesome. Ian, tell us a little bit about GenomOncology in your own words. What’s the mission of the company and where has it found success?

What GenomOncology Builds

Ian: Yeah, great. So yeah, GenomOncology is about 14 years old 14 years old now, uh based in Cleveland, Ohio. We’re a precision oncology company. Our main product and go-to-market is around pathology, molecular pathology. So it’s helping clinicians, molecular pathologists, take data from devices, whether it’s next generation sequencing, IHC, FISH, these are some older modalities, taking that raw data and then turning them into actionable insights. So, our software helps them understand the quality of the data they have and then interpret that information and then generate recommendations for helping a patient downstream. And so, we generate reports, but we also have to integrate with back-end systems. So, that’s, you know, these laboratory systems and the EHR and and other back-end systems.

Our software is all on prem, so we’ve been, you know, we since the beginning, uh where, you know, I’d say 90% of our clients were actually on physical hardware that the client owns, slowly moving towards, you know, virtual private clouds that they manage and control and we’re cloud agnostic, so we can support, obviously, Amazon and Azure and Google and anything else that people have. Uh and then a few clients that where we do all the soup-to-nuts hosting for them, as well. And so, that’s our main product line. It’s really, you know, how do we help a molecular pathologist do their job but do it faster and trust the information.

And then since then, we’ve been getting into like, basically, growing our business around that center core competency that had that had its root is really, you know, good old-fashioned AI. So, we have a knowledge graph, right, where we have diseases, drugs, genes, mutations, all organized in in what’s called an ontology or a graph of of information that we can then make recommendations off of. What we we called inference, right? It’s it’s a different type of inference, but you’re basically given a patient record, the patient’s the query, do inference off this graph and generate a set of set of recommendations and insights. And so, that that thing has been around since, basically, 2013 when I designed and built the first version of it.

And that that one capability, which we call match, uh we’ve used in a variety of different other modalities. So, there’s a there’s a exercise called a molecular tumor board or tumor board at a at a major cancer institution where the hardest cases go, right? They’ll review, you know, 10 cases a week of the the hardest cases that doctors can bring their patient to tumor board, and then our software helps them organize the information and make presentations so that they can actually understand what is state-of-the-art and what’s available and what are the clinical trials for it. And then we have analytics, right? You know, sort of like a Tableau or, you know, a BI tool, but it’s genomics-aware, right? So, it actually understands how the, you know, genome works and it understands all these ontologies.

And And we also do what’s called clinical trial screening. So, helping, you know, helping the matchmaking process, right? Where you have a, you know, a thousand patients a month coming into your clinic and you have a hundred clinical trials that are ongoing. How do you do that matchmaking? Uh it’s a sticky problem that we’ve helped with for years using our that same knowledge base. And now we’ve got agents. Um and with agents, it’s really what’s next. Uh how do we take agents and make all the stuff that we do that’s really powerful, but just make it easier, right? Make it faster, more accessible, and and really drive the results at places where they don’t have the staff necessarily to maybe integrate a complex solution uh into their into their daily workflows.

Harder Than It Should Be

Dev: This is really fascinating. I mean, there’s a few different areas that it sounds like you’ve been invested in. Like, I heard around analytics and dashboarding and reporting, clinical trial matchmaking, which I know is actually a large business in and of itself as well. And then being able to support the new kind of AI agent infrastructure. I want to talk about BioMCP in a few minutes. Before jumping into that though, I’m curious across the different essentially almost um offerings inside of the product, was solving one of them more challenging than you initially anticipated? Or did it all kind of flow naturally within the initial knowledge graph?

Ian: Yeah, I mean, it’s it’s all more challenging than it should be because healthcare just hasn’t kind of moves at a bureaucratic speed, which is fine. You know, we’re we’re we’re we’re trying to be that change agent helping folks do that. Um you know, that initial generating a report, you know, 13 years ago, understanding the different variants. We thought we had the interpretation that it was going to be, “Oh, you look at the variants and then you just make a report with all the variants on it.” And we realized that basically has no economic value. What you need to be able to do is think about the patient holistically, understand their disease, prior interventions, and other information about them. Yes, you need to know what the variants are and what the interpretation of those variants are, but you that then need to like look at it holistically to then say, “Oh, here are the Here are the therapies that are available, right? Here are the guidelines and here are the trials.” It doesn’t make any sense to tell somebody about um you know, you can tell them, “Hey, they have this this variant that’s not a great variant and there’s no drugs and there’s no trials.” Right? That’s That’s doesn’t leave the person with a lot of options. So, you want you really want to uncover all the information for that patient.

And then what we realized quickly is “Okay, we’ve got this knowledge base, we can match against it, but we need to know what the patient’s uh record is.” And, you know, think back to 2015, the patient’s record is a PDF or a set of PDFs or some text that a doctor typed out. How do you actually get that text into something actionable? So, we really invested a lot of time in the 2015 to 2020 era of how do we use OCR? How do we do basic NLP? So, I was using technologies like spaCy and and and and other things to do named entity recognition and and and linking to our knowledge base. And that was really hard. I I I looked at these reports and thought, “Oh, yeah, I can find all the entities. I know what this is.” And then you try to do it in practice and you realize, “Oh, wow.” You know, people are excited about 30% accuracy and that’s obviously not going to fly in the face of you know, trying to help a patient. So, we had to build a bunch of human in the loop systems for helping our clients you know, label this information or extract this information, but do it at a at a speed that actually was cost-effective. And that was that was a major that was a major challenge.

And now with LLMs, I’m seeing every single point along the way, right? At you know, starting with GPT-3.5 Turbo, trying to take that thing and say, “Okay, can this thing accelerate what we did with named entity linking and named entity recognition?” Uh and it’s that has been a a slog as well. And it’s really it’s kind of like everything else, you could really see how it’s turned a corner with first the reasoning models, and then kind of the Opus 4.5 and and on moments where you can really see, “Oh, yeah, these problems are all going to fall away.” Which is great. I I mean, I don’t want to be in an NLP business, right? I was in an NLP business for 5 years. It was a terrible business. Nobody’s happy. I want to be in a I want to be in a world where NLP is a solved problem, and now I can actually do the genomic insights and and take advantage of all that great structured data that we can now get for our for our clients.

Why NLP Was a Terrible Business

Dev: Why was NLP such a terrible business? Uh and I’m actually really curious about the practical reality of starting an AI-oriented company in 2013-2014, pre-jerk of AI, but post-deep learning. Um and so you had libraries like spaCy and some early transformer architecture models, and maybe like that intermediary {slash} beginning period of when you were probably um jumping into this. I had a guest on uh 2 weeks ago who’d also started a company, actually I think, you know, around the same time in customer support. His experience was like at a certain point basically had to throw away everything that they had been developing for years, and really move towards like the net new stack built on top of LLMs. But tell me a little bit about why like building, you know, in an NLP space was not the best business, and then what was the practical reality? Did you have to rip and replace, or did you some like incremental add-ons with LLMs?

Ian: Yep. So, yeah, so it’s hard. NLP is hard just in general. And then NLP in healthcare is extra hard. And then in NLP in healthcare where you do what we do, which is not trade software for data. So, one of our fundamental premises of a company was we’re tools company. We help our clients make solutions that make them a learning organization that help their patients. And that at the end of the day, the data is really the patient’s data, and the caregiver of that data is the hospital. We have competitors who go in and take the opposite approach, which is like, “Oh, uh we’ll give you this $0.05 of value, and we’re going to take your data, and then we’re going to go sell it for $10 of value behind your back.” And that to me has always just been uh gross. So, so we didn’t do that. We weren’t in the business of we’re going to do this thing, harvest your information, and then try to build a product off of it. And never did that, never would do that. So, and then on top of all that, HIPAA and other regulations that are actually good, right? Cuz they help protect patients’ data and protect them from payers knowing about what their sicknesses are, and therefore denying them coverage.

Dev: Mhm.

Ian: Those are all good things. So, but it makes our job really hard cuz what you realize real quickly is that NLP is a labeling problem, right? You really need to What you need to have is a strong model that’s not too expensive to run, right? So, the BERT-based models are still actually really good at named entity recognition. Like there’s a zero-shot library called Galiner, which is fantastic. Like you give it some labels, and you say, “Here’s some text, go ahead and do your thing.” And I was using Ludwig back in the day, right? Like, “Hey, how do I take this data, and like how do I train it using, you know, some some fabricated information, or can I train the data off of publicly available PubMed articles and uh clinical trial documents, and then make a model that actually can parse a clinical note?” You can’t. Like it it just it You certainly couldn’t in 2018.

Dev: Mhm.

Ian: Um so, it was just a really hard problem. So, we would basically, the way we did it was an ensemble approach. And the ensemble approach was uh we’re going to take the best models we can, right? The best BERT-based models we can, do as much as we can with them, but then we’re also going to build out um basically, for lack of a better term, rules-based approach. And the rules-based approach was, okay, we’ll have a set of regexes over here, right? Patterns for things like measurements, invariants, and other things that kind of follow a pattern. Great, dates. Yeah, we can we can go parse the dates. Obviously, you have to deal with European dates versus American dates, but other than that, you can kind of get something somewhere with those types of regexes. But beyond that, anybody trying to do regex, cuz I tried, you’re you’re you’re it’s a trail of tears.

And then on top of that, what I would do is, you know, take ontologies, right? There’s there’s this thing called UMLS, which is basically a dictionary of all the terms, right? All the drugs, diseases, etc., and all their synonyms. And then what you try to do is you take that, and then you basically make uh what you know, what’s called uh uh I don’t remember what the term is now, but there’s a algorithm called Aho-Corasick, where you basically take text, and then you can go find all the terms in the text. And you can make a really fast one, and it can find all the terms, and then you realize very quickly that you either have a false negative problem or a false positive problem, where you either accept too much, and you’re just generating too much noise that you have to filter through, or you have a or you’re missing too much. And so basically, then you have OCR on top of it. And OCR back in the day with Tesseract and some of these other technologies, they’re just a mess, especially when you’re dealing with a scan of a fax. Tesseract in like three.

Dev: Yeah. I feel like at least. At least.

Vision Models Change Everything

Ian: Right. And so now, you flash forward So then we basically built an ensemble thing, where we take all these different technologies, kind of merge them all together as best we can, and then show it to a human, and then try to build a user interface for the human to just do their job as quickly as possible to get rid of all the stupid mistakes, and then now you’re now you’re cooking, and and you have a process. And you can, you know, but once again, nobody wants to pay a lot of money for this because it doesn’t have a that much economic value compared to all the work you just did. Uh at least not at smaller scales.

But now you flash forward and now you have vision models, right? There’s obviously chat GPT and Claude and all these other guys. You give them a PDF and they’re just like, “Yeah, here’s the markdown of it. It’s perfect.” Uh it just depends on how much you want to pay. Uh and then there’s also these smaller models like uh the ZAI company in China. There’s a GLM model that they have a vision model and I was just like blown away. I have this I have this uh path pathology report that’s in the public domain that’s terrible. I’m just like, “How did they even make this thing so bad?” Like it’s like a facts of a copy of a something and you barely can read it. I can’t read it. You give it to this this little model that basically runs for millions of a penny and it can it generates perfect markdown. So that problem is solved, right? That was a problem I had to deal with for, you know, weeks, maybe months. That’s just a solved problem and now you’ve got the thing where you’re saying, “Okay, great. Go take this text and turn this into a little graph, a little knowledge graph of triples and that strategy for me works the best. So if you’re trying to do this at home, convert it to triples and now you got Now you’ve got a little knowledge graph of that document, whatever it is, and now from there you’re off and and cooking.

And so then our basically our value add on that now is just the linking. How do I link that to those underlying ontologies for there’s these these all these coding systems in in the medical domain, ICD for diseases and SNOMED and and all these other things. So So once you’ve normalized and you’ve linked it to these underlying things, now you can actually understand what the patient is and then you have to think about it longitudinally cuz the notes are kind of a mess, too. You have to figure out, “Okay, I gave this patient this drug at this time and then later on I give them this other drug at this other time.” You have to infer, “Oh, when did I stop this other drug?” And why did I stop this other drug? Those types of problems still remain, but man, the large language models are really good at it where an NLP engine just broke down right off the bat, right? Because they could never tie together this event over here and this event over here having anything to do with each other. And so the large Go ahead.

Rip Out or Keep?

Dev: Yeah, in practice what I was going to say is like the system that you were describing kind of at the beginning, Tesseract OCR, you know, combination of different models that may or may not work that well on NER tasks, and then probably needing to be able to fine-tune them, which is maybe where you were using Ludwig’s compositional model interface or you know, needing to potentially multi-shot them. Like that sounds brittle and painful, right? And I think people forget like 4 or 5 years ago that was like the absolute state of the art for really what existed.

One thing I’m really curious about is from a business perspective though, 4 or 5 years ago that sort of pain was some of the technical value and differentiation in terms of like what you created, right? Like you had built the ensemble of like what rules need to apply on top of what models. Now it’s feed it to Opus or Sonnet and like get some of those same underlying results like earlier. I think a lot of questions that people ask is are companies that are doing this kind of work now more valuable because they are able to actually operate more quickly than they would have, you know, previously and they can therefore serve a larger market or less valuable because they had an expertise in a developed system that sort of got upended with the new transformation wave. And obviously you’d have a stake in this, but I’m curious like how you think about it having built in the space and probably made the decisions about which parts to rip out and which to keep.

Ian: Right. Yeah, I mean never wanted to be an NLP company, did it because we had to to win some deals and to and to help some clients actually get their data cleaned up so they can actually use the stuff that we’re actually good at. So from my perspective, I didn’t really ever want to be quote unquote an NLP company. So and we had to compete with folks that, you know, they weren’t necessarily tied to this domain. They didn’t have, you you they did NLP across domains and the just happened to be one that they put, you know, in a drop-down on a website. So, we were competing against them regardless.

What we want to do is help folks, help patients maximize the the value of the molecular information cuz to me that’s the big bet. The big bet to me is that healthcare we’re kind of like walking around kind of blind still on how to help patients. And the reason why we’re blind is cuz we don’t really know how it all fits together. We don’t really know how uh you know, we have AlphaFold fold which knows how to predict a folding mechanism, but it still doesn’t know why it’s folding that way, right? Like we have all these mutations and we can tell you that yeah, that BRAF mutation’s really bad and that and oh, now we figured out a drug to help that patient. But in the future we know we’re going to with AI’s help, we know we’re going to figure out what this genome means and and you know, all the other types of omics data means to really help patients and that’s what I’m very excited about. And NLP is just something I’m happy to just put behind me and not think about ever again.

I’m also a software developer, right? I’ve been coding since I was 13 and I love software, I love building software. I don’t care if I ever write code again either, right? Like that wasn’t why I’m doing this. My goal is to help people and and the to help people is we can build software. Software is just one means of doing that. And that software can be NLP and it can be other things. What we’re trying to do is help people with you know, terrible diseases, right? Terrible diseases live longer and be with their families. So, that’s always been kind of like the division of what I’m trying to do with GenomOncology.

Healthcare at Bureaucratic Speed

Dev: And I want to ask you one question unrelated to AI, but actually relates building technology in the healthcare space for a minute. I’ve always been curious. I think a lot of my friends that have spent time uh especially in like core machine learning roles or others, they look at healthcare as a domain and they see vast amounts of unstructured data. Um you know, really highly economical like high high economics basically in the end value of what gets created. Right? Like medical code billing uh sorry, medical coding for billing is like some of the classic use cases where there’s a large amount of breakage and dollars lost just because of like coding mistakes or other things that could have gone, you know, clinical oncology things have been one where like people have been looking at working radiologists for quite some time to be able to see like can we augment or otherwise replace some work. So I feel like technologists from the outside of healthcare looking at healthcare as a domain, not experts, often look at it and they’re like, “This is a great place to be able to apply machine learning AI.”

At the same time, some of my friends that I think have started in this space hit some of the same things you mentioned, which is it’s not quite move fast and break things in my you know, in the Silicon Valley parlance. It’s not like a fast it’s bureaucratic I think is the word that you mentioned. There are rules for very good reasons that get placed, but oftentimes those are a little bit you know, in culture orthogonal to the way that a lot of technologists like to operate, which I don’t know if this was you, but I feel like when I was running code it’d be like, “Does it compile? No, okay, try again.” It’s just like, you know, guess and check is like kind of like the outcome and that’s less possible to do here. What was what’s been your experience I think as you think about like technology in healthcare? Would you say like it’s overblown like this concern around like the bureaucracy and the slow down of tech? It’s or is it underblown? Like where have been your experience in the velocity of what you’ve been able

Ian: It’s probably it’s probably blown, right? It’s probably right right in the middle. I mean it’s I think it’s correctly priced. Uh so yeah, healthcare’s tough. Like don’t get into health like we’ve been doing this for 14 years. Uh we’ve got a very good business now. I’m very I’m very happy with you know, the the the level of business we’ve got. We’re growing healthily, but it’s not going to be we’re not going to achieve entropic numbers anytime soon, right? So um now of course we do have that challenge coming up and we can talk about that, right? So open AI and and Claude they’re going after, you know, open evidence, which is a you know, pseudo competitor to us.

Uh so the really the question becomes like yes, is healthcare slow and bureaucratic? Yes. It’s actually beneficial to us, right? Because we’re installed at a large portion of the academic medical centers. We’ve got some major reference labs and other institutions that are our clients. We’re directly integrated into their workflows, so we got their eyeballs and we got their process and we’re there to speed them up every day. Like, hey, can I shave a minute off of their work process every quarter? If I can keep doing that, they’ll keep me around, right? Like, that’s the goal. The goal is for them to save time, because time is money and time is helping patients. So, so there’s negatives to it, but but the positive is once you’re in and have a trusted reputation and a trusted brand and you don’t blow that brand, right? You want to keep that that that brand brand strong, uh, it’s a it’s a good business from that perspective.

Frontier Labs Come for Healthcare

Dev: Hm. And tell me a little bit now, I think on the same topic you brought up, which is like the Anthropic opening eyes to the world. Opening I will announce ChatGPT health, um, and then I think, you know, Open Evidence has taken a large market share in terms of like the physician usage of LLMs, specifically with like a domain adaptation then towards health care. Um, and I think you see this kind of same type of paradigm in other fields, too, right? In legal, you see leading companies like Legora, Harvey as like vertical applications, plus like you then have Anthropic like trying to take on more of like the legal market as well. What do you think will be the most difficult for these foundation model lab companies to get right in order to be able to build a large scalable health care AI business?

Ian: Um, yeah, they’re never going to grow the Anthropic business and the Open AI business at the the run rate that they have now if they focus on my business, right? My business isn’t going to grow They’re not going to get that that growth rate. So, I’m not worried about them coming directly after me. Uh, Open Evidence seems like a great company, right? They’ve got, um, lots of great adoption. They’re kind of like the iPhone model, right? iPhone was, oh, IT companies IT departments would ban iPhones, but people just brought them into the door and what what are they going to do? So, you got to start adopting it. So, now the they’ve got the physicians. Open AI’s trying to catch up. Anthropic’s trying to catch up to them. Uh I’m I’m sure they’ll be very successful. They’ve got all the agreements with the publishers, right? So, one of the main problems with uh publishing in our country and in the world is you we pay for the science and then the science gets trapped behind paywalls. Whatever, that’s that’s something I you know, so BioMCP my I can talk more about BioMCP later, but BioMCP does access all the publicly available information. So, we can kind of compete with Open Evidence there, but once again, they have the licenses to these to these uh published published papers. So, open Open AI and Claude and Anthropic will need to be able to get access to those as well.

And then the question really becomes where do the IT departments make their stand, right? These tools are so valuable that the IT departments are going to have to let some of it in. And so, the the bet I’m trying to make is okay, what I want to be able to do is one, be a good consultant to these to these groups, right? We have trust, we have brand, we have partnerships with folks. So, I want to help them solve these problems, right? Come to GenomOncology, we will help you figure out we’re we’re vendor-agnostic. I’ll work with Open Evidence, I’ll work with Open AI or Claude. We work with all the major labs, we work with all the major device makers. We That’s not our our goal. Our goal is to help you make your organization work.

And so, what you need to be able to do is take publicly available information. You need to take these large language models, whether they’re the big frontier models because you your your use case demands it or oh no, this is a repetitive process, right? This is a prior authorization or this is finding a trial or a drug or whatever. And really the most important thing there is access to the right information and a really small model, like a Gemma 4 fine-tuned on on your that specific thing or even the Med-Gemini models are pretty good. Um you know, you can get that that small model running it locally pretty cost-effectively, tying it to tools, right, in an agentic loop, it can now do that that task, that job to be done. Given the inputs, do the job to be done. And it’s better than what our system is, right? From a from a flexibility perspective.

Our system’s great because I can not only is it uh explainable, I can tell you why I’m giving you the answer, and repeatable, I give you the same answer every single time, no matter how many times you ask me, you’re going to get the same answer. An LLM will won’t do that. No, even if you set the temperature to zero, that doesn’t You’re going to You’re going to get a different answer every time. But what you need to be able to do is figure out how do I tie this with tools so I can get that explainability, get that repeatability, and then how do I do it on prem so I’m protecting my data, I’m I’m protecting my patient information, HIPAA compliant, and and how do I integrate it with all my systems, right? EHR, LIMS, CTMS, whatever these different acronyms are that I’m sorry that I’m dropping on you guys. Uh that information is core to these businesses for getting their jobs done. And and we, you know, we love helping our clients do that stuff.

Agents, Tools, and BioMCP

Dev: Help me understand I know that you just talked a little bit about being able to connect some of these models to some of our some of the smaller variants towards tools. That’s often times how we define agents. Like in, you know, you ask four different people an agent definition, you’ll get six definitions. I often just think about like agents now as models with access tools so they can go ahead and like take an action. It’s not just purely like generation or synthesis, it might actually be making like a function call at the other some parameterization. Like I’m curious like A, how do you define agents? And then B, um where have you seen the greatest lift from agentic workflows? Cuz part of what you were doing, you know, here is basically a better faster mouse trap. Like you had certain versions of models probably starting in 2016, 2017, and now you can do that work with much less training, you know, with the with the latest like the large language models today. But that introduction of like tool use for models is different. So where have you seen the biggest lift from models accessing tools in healthcare?

Ian: Yeah, it’s I mean it’s coming, right? So I’ve been talking about agents for a for a while. Everyone has, obviously. And and I’ve been trying to figure out how do I blend these things with my tool set uh from the beginning. And so our system is API first. So I built all these systems, they’re all they all have APIs to them. All the user interfaces all sitting on top of the same APIs. Uh so when GPT plugins came out in April of 2023, I was using those. I was using custom GPTs which could plug into your open open API swagger based uh API. I was using those in custom GPTs. And then when MCP came out in 2025 now? I don’t even 2024, sorry. Time’s evading me. End of 2024, MCPs came out. I was kind of like, “Eh, I don’t know if I want to use these.” Just cuz I’ve been been through that rodeo. But then you realize, “Oh wait, Sonnet 3.5 actually works with this thing. Like it can actually know how to call the tool.” And that was kind of the great unlock.

So that’s when I invented the Bio MCP open source project. So Bio MCP is basically a connector to all these open source data sets from PubMed articles, all the variant information, genes, diseases, drugs, pathways, all the biomedical information. If there’s something that’s out there that I haven’t added, send me an email, I’ll add it to it. So I invented Bio MCP. At first I thought it was an MCP model context protocol. And I was like, “Okay, this is going to be the kitchen sink model context protocol.” And I basically added 35 tools. And then I realized, “Oh no, this like loads up your context and slows you down.” And context right now is the most important thing. Like you got to have the smallest context possible while giving access to the tools. So I actually refactored the whole thing recently and now it’s a CLI first, so you can plug it into Claude code and and it’s still MCP, but it’s just one of the CLI calls to start up the MCP. And that works great. So, if you’re using cloud desktop, use MCP. If you’re using cloud code or using codex, use the CLI version. Or if you’re using Pi, cuz Pi is actually the best coding agent, uh you plug it in with Pi and it works great, too.

So, okay. So, what’s an agent? An agent is, as you said, an LLM in a loop calling tools with a goal, right? And a set of skills now. So, you give it a set of skills and you give it a goal and you give it a set of tools. And so, the abstraction layer that I’m working through is So, I have my coding agent, which helps me with all my projects that I’m working on, and it has those set of skills. I have a marketing agent, right, that gives has me helps me make slides and stuff. And it’s got its own set of skills and its own set of tools. And now I’m building biomedical agents as well. And starting with here’s a set of here’s one set of skills for doing trial matching, right? Helping a pa- given a patient, how do I find a trial for them? Or given a trial in a database of patients, go find me patients that might be eligible for that for that trial. So, that agent, right, has is encapsulated with a set of skills, right? The skills tell you how to use the tools effectively. And the tools are, you know, tools for accessing bio MCP or accessing our knowledge base, really, right? Locally for trials and therapies and and things like that. Tools for how to understand biomedical information, right? Molecular data, accessing clinical data from an EHR. So, we have a tool that can talk fire, uh which is a biomedical or it’s a medical standard for talking to EHRs and other systems.

And so, that agent is really the encapsulation of a subset of tools, cuz you don’t have you don’t want to give it everything. You want that agent to be focused on that task or set of tasks. And and so, you’re minimizing the amount of context that they have to like devote to getting up and running. But the awesome thing is you can give them a task and then they can figure out it’s not a workflow. So, one thing that some people keep going back to is, “Oh, it’s got to call this step and then call this thing and then call that thing. That’s not what you’re supposed to do with agents. If you want to make a workflow, make a workflow and you can use an LLM in that sense, right? Where basically you’re calling one system, putting it into a LLM and getting the results out or maybe you’re doing rag where you’re basically giving it a database and and it’s just a question answer machine. That’s not the power of an agent. The power of an agent is I have a task to do. It’s not clearly defined in that I don’t know the actual steps we’re going to take ahead of time to actually solve the problem. And then it’s going to call tools and a tool in this case is for the most part means it’s finding some information, searching or getting some information and then taking some action. And that action just might be write a report, right? Here’s the report. I want to give my patient this test or I want to give my patient this drug. Please create an authorization letter. So I can give it to the insurance company so the insurance company can say, oh yes, that drug or that test makes sense for this patient. Please go ahead and do that.

Code Goes to Zero

Dev: What’s an agent use case that you’re excited for somebody to build with BioMCP that doesn’t exist yet?

Ian: The one I don’t know about. So I’ve made BioMCP a marketing device, right? So in my opinion, all code goes to zero. Sorry to the coders out there. The value of code itself by itself is zero. The value you add as a software developer is that you stand behind the code, you can help drive the code and that you understand how it all fits together and that you’re solving a domain problem. So code goes to zero and public information goes to zero. If it’s out there on the web, Claude will go ahead and build a tool that knows how to do curl and grab it from the web and and then answer it. The reason why BioMCP exists, and this is what the paper is going to say, the reason why you use BioMCP is because it’s going to use less tokens and it’s going to do it faster and it’s going to be more reliable and it’s always going to it’s going to give you more consistent results because it’s using a known set of of of known resources. So you use BioMCQ rather than letting Claude go figure out, “Oh, I found clinicaltrials.gov and now I’m going to whip up a clinicaltrials.gov client right in Python.” Yes. Well, why do we need to do that a thousand times a day, right? So, just use BioMCQ. It can do all the searching on clinicaltrials.gov and it can find those fields that Claude’s going to skip over.

So, all the public information though is basically worthless effectively from a business perspective. It’s great for a patient perspective. There’s lots of value that’s going to be unlocked for patients and doctors and stuff like that. But from a software company, I’m not going to make any money from these public sites. So, that’s why I’ve given these things away for free. But what that unlocks is latent demand, right? Okay, I’ve used all these things. Hey Ian, you made BioMCQ. It’s great, but now we want to use it with my private data or I want to use it with my EHR or I want to use it with whatever or your knowledge base that we’ve hand curated. That’s the unlock. That to me is the the the use case that I want to see. Uh and really what I want to see is, you know, how can we either if it’s public, right? I’ll add it to BioMCQ. Or if it’s private, let me help you do agents at your organization, agents that you can trust, agents that have repeatable answers and are explainable and all that other stuff. So, that’s the goal for me. And then really honestly, the end goal is uh I mean I want to make money. I want our company to be successful. I want to help people. Like I just want our company to be a a a source of good in the world and help uh help in this fight against cancer.

Labor Displacement and Caregiving

Dev: On that idea, I know that this is uh a really popular direction people like to take things when it comes towards agents, but I’m curious about your opinion on like labor displacement specifically in healthcare. Cuz if you say like code goes to zero and we say public information goes to zero, it’s hard for me to listen towards like what BioMCQ could enable plus like the rest of, you know, the prior authorization use case there that you kind of mentioned, you know, medical coding as an example. There’s lots of different I think inefficiencies that also probably mean jobs today. Like these are things that have to be manually done. Um and I think you’re seeing this kind of disruption in a lot of different markets. If you were to take a medium-term view and I’ll think I’ll take think of the medium-term as like over the next 3 to 5 years. What are the domains within health care that you think will have the most staying power through AI? And are there some one or two areas that you think, oh, in 3 to 4 years like we’re going to see 20% employment of what it is today?

Ian: I mean it’s it’s caregiving, right? That’s what health care is. People go into health care to be caregivers or to do science. Like there’s a whole branch of health care that they don’t talk to patients, right? They’re You know, I’d be that person. I’d be going over there Let me not help the patient directly. Let me help the person that’s helping the patient. And to me there’s it’s infinite demand. There’s infinite work that needs to be done to help patients, right? Cancer is There’s There’s been amazing progress on the battle against certain types of cancers, less against others, right? Pancreatic cancer has been a terrible disease. My grandfather passed away from it. It’s basically incurable. It seems like it’s it’s extremely aggressive. It’s not very easy to treat, right? With pancreatic cancer.

Dev: Yep.

Ian: And there’s you know, one set of genes. There’s the KRAS gene for instance has been notoriously undruggable. And there’s a recent drug that is making a lot of headlines, right? Doubled doubled the overall survival length, right? From 6 months to 13 months or whatever it is from a median survival. There’s former Senator Ben Sasse that was on 60 minutes this week talking about his experience. And it’s just been great for him, right? It’s decreased his pain. And it’s adding months to to to his life that’s been greatly shortened. He’s He’s basically said I would be dead by now if it wasn’t for this drug. So, that is That’s why we’re here, right? And I think that there’s infinite work there.

Now, is there going to be displacement? Yes. I think I think that just in general for AI, I think everybody I guess developers are kind of here first cuz we’ve seen how these models work, how they can actually improve your work. But the other thing is developers also are very good at software development. They’re not necessarily good at economics and how diffusion works and I think it’s going to take time, right? So I think I think these models are going to make us more productive. I think we’ll have time to react to it. I think that there’s always unfortunately always ups and downs with the economy and that plus AI plus a plus an election, all those things will probably mean that there’ll be some job displacement and then it’ll get blamed on AI. I think in healthcare it’s one area where I I have no compunction. Like I’m very excited about working on AI for healthcare cuz I’m helping people directly. Will Will somebody who’s doing medical coding right now be out of a job in 6 years? Maybe, but from what I understand, they can’t hire new medical coders. Like for whatever reason, I think that’s actually a job that’s like has aged quite a bunch. Like they used to be 30-year-olds, now it’s 50-year-olds. And so getting new coders, I don’t even know if that’s like a job that people would necessarily want. I don’t think it’s super exciting to do, to be honest. So I think that that’s a a great job to actually try to automate. And how can that same How can that same dollar and that same person be used in a way that helps patients more directly? And I I think AI can help us there.

Overhyped, Underhyped, and the Jevons Paradox

Dev: Ian, you know, before I started Predibase, which was an infrastructure company, one of our investors asked me, “What do I think is an overhyped and underhyped area in AI infrastructure?” Now, I’m going to ask you the same thing in healthcare. Like in healthcare AI, there are so many different types of applications towards AI as well as like tooling layer, like types of applications. What do you think are Like what do you think is one place in healthcare AI that you think is overhyped and one place that you think is underhyped? If you were to put almost like an investing hat on.

Ian: Oh, man. Um I think that that’s I don’t know if I have a good answer for that. I don’t know if I have I don’t have a good answer for that one. Let me think. Give me a minute here, Dev.

Dev: No problem. I thought it was really challenging. While you think, I’ll actually just give you a little sense of like how I thought about it in the past. I thought one thing that was overhyped at the time was these like wizzy wig low code like no code I should say no code model builders. You know, this all the rage in 2019 2020. I thought they were a little bit overhyped because it’s hard to figure out how to be able to plug these into your actual workflow once you had a UI kind of build your model. It’s like did you know how to be able to use it downstream. An area I thought I was that was underhyped in 2019 was data labeling and data infrastructure. I thought it was an unsexy beast that every organization had to do to make a machine learning that actually worked and I think it’s, you know, consistently is true with AI today. I will say you don’t need to give me like necessarily one of both, but I’m curious if either on the underhyped or overhyped side you have a tick on something that you think is getting more attention than it should or not as much attention as it deserves.

Ian: Yeah, so there’s a couple different areas. So I think the one so we’re in this business of clinical trial screening. Right? So and there’s I’ve seen at least 20 papers if not more and there’s a lot of companies that are doing clinical trial screening similar to what we do. And the the idea that people have is oh, what we can do is we just read the the LM can read the clinical trial and then it can read the patient and basically say can this patient go on this trial? And the main way they do that is or one of the strategies I see is take this document the eligibility criteria which is still unstructured which is annoying. Like like hopefully the and you know, NCI or the clinical trials website maybe we could I don’t know maybe we can help some. If someone is there that needs our help, let us know. Instead of collecting the information in an unstructured format and then turning it into a structured set of questions that you can then ask. That’s kind of the strategy people do and there’s a lot of papers, there’s a lot of you know, there’s a lot of people with PhDs looking for papers to write. So they write a paper about this. And the the and which is nice, right? So hey, you take this trial and you ask the patient can this patient go on this trial? It’s just missing a few few things. Well, one if you have a thousand patients and a hundred trials, you’re going to do that a hundred thousand times. It’s going to be cost cost ineffective.

The way our system works is we kind of read the trial, codify that to our knowledge graph, and now and we then we take all the patients and we codify them to our knowledge graph as well. And then what you’re doing is now we’re you’ve turned that LLM problem into a search problem. And then and then now you’re doing it at sub-millisecond queries, right? Like, okay, go here this patient, go find all these trials and bring them in. The other thing you’re overlooking is the fact that there’s this data problem. The data problem is you have to be integrated with a CTMS cuz the clinical trial management system, cuz you don’t actually know what the trial’s recruiting status is because clinicaltrials.gov doesn’t have that. Um so there’s all these other technical problems as far as why uh matching isn’t just this pure you know, easy button thing with LLMs. So that’s one thing.

Uh the other thing is I get investors and folks that are investment in in GenomOncology sending me things like, “Hey, here’s this new test or this new modality or this new thing.” And it’s like, “Great uh great, you know, blog article, great paper. That thing’s going to take a while for it to get FDA approved. And once it is FDA approved, it’s already going to fit into our model, which is, you know, you’ve done this test, you’ve got this new way of recognizing a biomarker. Well, the biomarker is a biomarker is a biomarker in my system. We then just take that and then use it downstream.

Um so those are just two areas. And I guess the last one that that’s jumping out at me is the radiology comment from Geoffrey Hinton, right? Everyone thinks about this Everyone talks about this, but 10 years ago or so, Geoffrey Hinton’s like, “Don’t let your babies grow up to be radiologists,” right? Because there’s not going to be any radiologists. And the the problem is now there’s more demand for radiologists. And that’s just showing you, right, that someone as brilliant as Geoffrey Hinton, who’s so smart about AI, uh doesn’t understand necessarily that, okay, what we’re doing is we’re taking this person’s job, right? A radiologist. And they’re were at one narrow part of it where they’re looking at the pictures and trying to find out where the cancer is, and then extrapolating, oh, the AI is going to do that. That means their job goes away. Well, no, there’s so much other parts of the job from from, you know, before and after that one task that now that one task that was very cost costly now goes to zero, and now the demand for their services go way up because we can do it in a more cost-effective way. So, it’s the Jevons paradox brought to brought to healthcare. Um so, I think that that’s still something very relevant to to the medical industry. You know, I actually think that the same thing is actually going to happen in a few other disciplines. I know engineering we’ve all been talking about like the decline of software engineering. I think software engineering will change, but I actually think the aggregate number of roles that are available for people will grow. Um cuz I think now the same kind of builder mentality, if you if you were a good engineer, that’s kind of like one of the core things you wanted to do is you wanted to problem solve using a set of tools. It’s not like because you went from like low-level abstractions and like writing in ASCII or C to like, you know, higher-level programming languages, that the demand for this role uh declined. Then I think you’re going to actually see the same thing with some of these um coding agents as well. But, it’s, you know, great

Dev: Agreed. I think what’s happening in um in kind of the healthcare side of it with respect to AI as well. You know, I really want to thank you so much for uh you know, coming onto the show today and being able to walk us through both what it took in order to be able to build a company in a healthcare AI pre-generative AI. I’d say through the deep learning wave that happened in the late to uh 2017 to 2020 era. Then, of course, with the advent of like, you know, these generative pre-trained transform models and everything else that came through. It’s fascinating to hear your um perspective and also to hear, candidly, you want to make money, but you want to help people. That’s like a huge uh you know, motivation for why you’re in this game. I’m going to check out the BioNLP project in more detail after this, and I can’t wait to revisit with you in 3 to 6 months and actually find out what was one of the best things you’ve seen someone build with the tool. So, thank you so much for I think uh joining us here at the conversation today, and really look forward to seeing where the rest of the future goes with health care and AI.

Ian: Thanks, Jeff. Thanks, Jeff.

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