As a medical student who also writes code, I spend a lot of time thinking about where these two worlds intersect. Medicine is drowning in information — thousands of pages of guidelines, decades of research, and an ever-growing body of clinical knowledge that no single person can fully internalize. That's where AI tooling comes in.
The Problem with Medical Learning
Traditional medical education relies heavily on memorization. Students read textbooks, attend lectures, and cram for shelf exams. But the real skill of medicine isn't recall — it's pattern recognition and clinical reasoning. You need to look at a set of symptoms, lab values, and imaging findings and synthesize them into a differential diagnosis.
This is exactly the kind of task that AI can augment. Not replace — augment.
What I've Been Building
Over the past year, I've been working on tools that use large language models to support clinical learning:
ChartLens
ChartLens is an LLM-powered clinical chart review tool. It takes unstructured clinical notes and helps students identify key findings, flag potential issues, and practice the kind of systematic chart review that attending physicians do naturally.
The technical stack is straightforward:
// Simplified example of the chart analysis pipeline
const analyzeChart = async (notes: string) => {
const structured = await extractFindings(notes);
const flags = await identifyCriticalValues(structured);
return { structured, flags, summary: generateSummary(structured) };
};
The challenge wasn't the AI part — it was designing an interface that teaches rather than just answers. If you just hand students the diagnosis, they learn nothing. The tool needs to guide their reasoning process.
Key Design Principles
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Scaffold, don't shortcut. Present information in layers. Let students form their own hypotheses before revealing AI-generated insights.
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Show the reasoning. When the AI flags something, explain why. "Elevated troponin in the context of chest pain and ST changes suggests..." is more educational than a bare alert.
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Stay grounded. Medical AI must be conservative. A wrong suggestion in a learning tool can create dangerous misconceptions. We validate outputs against established clinical guidelines.
Lessons for Health Tech Developers
If you're a developer thinking about building tools for healthcare or medical education, here's what I've learned:
- Talk to users constantly. Medical professionals have specific workflows and mental models. What seems intuitive to an engineer often isn't to a clinician.
- Respect the domain. Medicine has centuries of accumulated wisdom encoded in its practices. Don't assume technology knows better.
- Design for trust. Clinicians need to understand why a tool makes a recommendation. Black-box AI is a non-starter in healthcare.
- Start small. Pick one narrow workflow and do it exceptionally well before expanding scope.
What's Next
I'm currently exploring how retrieval-augmented generation (RAG) can be used to build study tools that reference specific textbook passages and clinical guidelines. The goal is a system where students can ask questions and get answers grounded in authoritative sources — with citations.
The intersection of AI and medical education is still wide open. If you're working in this space, I'd love to connect — reach out via the contact section of my site.