Welcome to HippocrAI: The physicians oath, in code.
A physician-founder, an open-source library of AI agents for medtech, and the discipline that keeps them safe.
What HippocrAI Is
Last month, I built an AI agent to draft my own NIH SBIR Specific Aims page.
Three tools — a PubMed search, an NIH RePORTER search, and a self-critique loop — are wired to Claude via a system prompt that hard-codes the canonical NIH aims structure. I gave it a project briefing and let it run. A few minutes later, the draft needed only verification before it was ready for use. The code is now public on GitHub. The companion essay explaining how I built it goes up next.
This is the publication where work like that gets shared, with the code and the reasoning intact.
It’s also where I write about the rest of medtech founding from a cold start — accelerators, engineering partnerships, regulatory pathways, IP, the unglamorous infrastructure that consumes the actual hours of building a medical device. The agents I publish are mostly tools for that work.
The premise is simple: the writing-heavy, format-rigid work that consumes a disproportionate fraction of medtech founders’ time is exactly the work that AI agents are good at — if they’re built carefully, scoped narrowly, and grounded in real source material. Those last three conditions are where most public AI tools fail. The discipline of meeting them is what HippocrAI is built around.
That’s the short version. A longer essay on what that means, who I am, and why this is open source rather than a startup follows.
Who I am
I’m Joseph Hayhurst — MD with a concentration in economics, with prior training in general surgery, currently focused on medical device development through Hayhurst Medical Technologies, LLC. Engaged with multiple investors in the regional medtech ecosystem. Provisional patent filed on the device with plans for an eventual non-provisional.
What that adds up to in practice: I’m a one-physician operation building a regulated medical device, with a calendar that has to absorb everything from CAD review to grant drafting to investor updates. The agents I publish here exist because I needed them — first for myself, then because it became clear the same tools would be useful to anyone else in this position.
Most physician-founders are in the same situation. Most early-stage medtech companies are. The infrastructure for getting from “I have a device” to “I have funded development” is mostly writing infrastructure: SBIR aims pages, FDA pre-submission documents, literature reviews, IP filings, regulatory pathway analyses, investor updates. None of it is glamorous. All of it is rate-limiting. AI agents — properly built — can do most of it in hours instead of weeks.
That’s what HippocrAI is for.
The thesis
The most useful AI tools in medicine are not going to come from AI labs that don’t understand medicine, or from medical institutions that don’t move fast. They are going to come from physicians who have learned to use AI as a force multiplier and who hold themselves to the discipline the profession demands.
Five years ago, a physician without engineering training couldn’t build their own software tools at any meaningful level. Today, with frontier language models capable of producing most of the boilerplate when given a clear specification, the bottleneck has moved. It is no longer “can the physician write code.” It is “does the physician have the discipline to specify well, calibrate framing, enforce upstream guardrails, and verify output against reality.” That kind of discipline is what medical training produces. Most physicians who’d be skeptical of their ability to build tools have, embedded in their training, exactly the prompting discipline that produces good output from AI.
HippocrAI is what that work looks like in public. The architectures aren’t novel research — the patterns are well-known and any competent engineer could rebuild them in an afternoon. What’s rare is the combination of medical domain expertise, regulatory discipline, and willingness to build openly. That combination is mostly held by physicians who don’t yet realize they could build their own tools, and by engineers who lack the domain context to know what to build.
The agents are free. The discipline is the moat.
What HippocrAI stands for
Four pillars that define what gets built and what gets released.
Open by default. Every agent ships with code, prompts, and the reasoning behind both. Apache 2.0 license. No paywalled “premium” tier — that’s a different business model and not this one. Open-source isn’t a marketing tactic; it’s a structural commitment that forces the work to be honest under public review.
Physician-led. The work is grounded in clinical context, not pattern-matched from afar. Every agent passes physician review before public release. As contributors join, the goal is that contributor agents will get reviewed by physicians on an editorial board. This is the credentialing infrastructure that AI-only or non-physician medtech tooling can’t easily replicate.
Medtech-specific. Not general medical AI. Not a general developer AI. The vertical is medical device development — devices that go through FDA pathways, NIH funding, and the regulatory and operational stack that comes with them. Specificity is what makes the agents actually useful.
Built under the oath. First, do no harm. Every public agent is unambiguously not a medical device, not clinical decision support, not a substitute for a clinician. Every release goes through a written pre-release review protocol with hard guardrails — provisional-patent firewall, FDA-compliant framing, no real client content, generic prompts only, explicit human-in-the-loop verification. The discipline is what protects the integrity of the work.
What HippocrAI is not
This part matters. Public-facing AI in medicine has a real problem with overclaiming, and I don’t intend on adding to it.
The agents published here:
Do not analyze patient data
Do not produce diagnoses or differential diagnoses
Do not recommend treatments
Do not calculate doses, clinical parameters, or risk scores
Do not triage patients or assess clinical risk
Do not produce any output that could reasonably be characterized as clinical decision support
The acceptable surface for HippocrAI tooling is administrative, regulatory, scientific-writing, and operational work. SBIR aims pages. FDA pre-submission documents. Literature reviews. Predicate-device searches. IP and patent drafting support. Investor updates. The kinds of writing tasks that take medtech founders weeks and that AI agents — properly built — can do in hours.
Nothing on this site is medical advice. Nothing on this site is legal advice. Nothing on this site is a substitute for an attorney, a regulatory consultant, a biostatistician, or a physician.
What to expect
Three categories of post, with each new agent shipped alongside its companion essay.
Agent build essays. Walk-throughs of an AI agent I’ve built for medtech work — the architecture, the prompting patterns that turned out to matter, the design tradeoffs, what surprised me. Code on GitHub, linked from each post. The first one — I Built an AI Agent to Draft My Own NIH Grant — drops next.
Pattern essays. When I notice something general about agent design, prompting, or AI in regulated industries, I write it up. The next one in this series, Discipline at the Input, walks through seven prompting patterns that any domain expert can use to build their own tools — with a focus on why medical training is unusually good preparation for this kind of work.
Medtech founder field notes. What it looks like to build a medical device as a solo physician-founder from a cold start — accelerators, engineering partnerships, regulatory pathways, IP. Specific, honest, useful for the next person doing this.
Posts arrive roughly every one to two weeks during launch, settling into a more sustainable cadence after the first three. Subscribe and they come straight to your inbox.
Why I’m publishing this in public
A few reasons.
The work is more rigorous when it’s reviewable. Closed AI tooling in regulated industries is exactly where you don’t want bad incentives. The cleanest way to keep my own incentives honest is to make every prompt and every architectural decision visible. The pre-release review protocol I run before any public release is a stricter discipline than I’d impose on myself privately.
The next physician-founder shouldn’t have to re-derive these tools from scratch. There are several thousand physician-founders building medtech companies right now, and the writing infrastructure they need is mostly the same. Releasing the agents openly is the highest-leverage thing I can do for that group, and it’s the work I’d want them to do for me.
Open-source is also the only honest answer to the question of who AI tools in medicine should belong to. The closed proprietary version of this project would be more lucrative in a narrow sense and would compromise nearly everything I think AI in medicine should be. So this is the version.
What success looks like
Not subscriber count. Specifically, it looks like physician-founders shipping better SBIR submissions, faster regulatory drafts, and cleaner IP work because of tooling that exists here. It looks like the regulatory consultant who tells me they used the literature-review agent for a predicate-device search and saved a week. It looks like the engineer at a medtech startup who finds the GitHub org and forks an agent into something specific to their device. It looks like another physician deciding that they, too, can build the tools they need.
If a few hundred medtech founders save a few hundred hours each because of work published here, that’s enough.
How to follow along
If you’re a physician-founder, a medtech engineer or operator, an AI engineer interested in regulated-industry work, or someone curious about what medtech founding actually looks like from the inside — subscribe. Posts come straight to your inbox.
The first technical essay drops next. The agent’s GitHub repository is already public.
If you want to reach me, reply to any email — those land in my inbox directly. The agents are free. The discipline is the moat. Welcome.
— Joseph
Joseph L. Hayhurst, MD Physician-founder · HippocrAI hippocr.ai · github.com/hippocrai ~ Joseph.l.hayhurst@gmail.com
The combination of medical discipline, open-source thinking, and practical AI tools could genuinely change how early stage medtech gets built
This is refreshingly grounded: narrow agents, source material, open prompts/code, and verification.
The next layer I’d want to see is consequence discipline: what the agent may do, what evidence it needs, when it escalates, and when it must stop.