Stop Searching “How to Code Like a Software Engineer”: A New Path for Physician-Developers
By Chukwuma I. Onyeije, MD, FACOG
Medical Director, Atlanta Perinatal Associates | Creator of CodeCraftMD & PreEclampsiaWatch
Why Doctors Search for Coding Courses (and Why It Slows Them Down)
Many physicians are also becoming doctors who code, as they recognize the importance of technology in healthcare.
A familiar pattern repeats itself in the medical community. A physician decides they want to build a tool to improve their workflow, so they open YouTube and search:
- “Python for beginners”
- “How to code like a software engineer”
- “Full-stack developer course for doctors”
Three hours later, they are stuck in “tutorial hell”—watching, pausing, rewinding, and absorbing abstract vocabulary without producing anything meaningful.
This happens because doctors are trained for applied reasoning, not abstract theory.
Clinicians learn best by solving a problem they care about. We retain knowledge through cases, not through syntax lists. We thrive when tools accelerate our clinical decision-making, not when they force us to memorize documentation.
The old pathway—memorizing syntax first, building later—works for CS majors. But for physicians learning to code, there is a better way.
The Pivot: From Coding Tutorials to AI-Assisted Building
Today’s physicians do not need to become traditional software engineers to create real, HIPAA-compliant tools. The landscape of medical software development has shifted.
Instead of raw coding, physician-developers can now leverage:
- Large Language Models (LLMs) like ChatGPT and Claude
- Code reasoning engines
- Agentic assistants
- “Vibe-coding” workflows (coding by intent rather than syntax)
The Shift in Methodology
| The Old Way (Pre-2024) | The New Way (2026) |
| Learn Python syntax | Define a clinical problem |
| Learn frameworks (React, Django) | Build immediately with an AI Agent |
| struggle to build a “To-Do List” app | Learn concepts “Just-in-Time” |
| Result: Burnout | Result: Working Clinical Prototype |
You learn by producing, not by preparing. This is exactly how my projects, like CodeCraftMD and the PreEclampsiaWatch app, were born—not through months of studying syntax, but through guided inquiry, AI reasoning, and rapid prototyping.
Why You Don’t Need to Be a “Software Engineer”
The question is no longer: “How do I learn Python?”
The real question is: “How can I build something useful for my clinical workflow?”
As a board-certified specialist, you already possess the core skills required for high-level programming:
- Strong analytical reasoning
- Pattern recognition
- Case-based learning
- Comfort with decision trees and algorithms
- Deep domain expertise
This gives you something junior developers lack: clarity of purpose. When you pair that with modern AI coding assistance, you bypass the heavy, front-loaded educational burden of traditional engineering.
The Vibe-Coding Method: A 5-Step Blueprint for Doctors
This method reflects how I build software today as a practicing MFM specialist.
1. Start with Your Clinical Pain Point
Do not start with a language (e.g., “I want to learn JavaScript”). Start with the diagnosis.
- Example: “I need a way to summarize handwritten blood sugar logs for my diabetic patients.”
- Example: “I need a consult-to-billing summarizer for the clinic.”
- Example: “I want a Kick Counter app for high-risk pregnancies.”
A project gives context. Context gives relevance. Relevance makes learning effortless.
2. Use an LLM as Your Mentor, Not a Tutorial
Instead of Googling “How do I build a React login screen,” prompt your AI assistant with clinical context:
Prompt: “Help me build a secure login screen for a Maternal-Fetal Medicine app using React and Firebase. Include sample code, explain the security protocols, and explain each section like I’m a colleague, not a developer.”
The LLM acts as a pair programmer that never gets tired, scaffolding the code and explaining concepts in language you understand.
3. Build First, Learn “Just-in-Time”
Doctors don’t need 600 hours of theory to launch an app. If you build a working prototype today, you will naturally absorb the necessary concepts:
- Asynchronous functions (like waiting for lab results)
- Database reads/writes (like charting)
- Authentication (like HIPAA compliance)
Every technical concept becomes a clinical consult: You learn what you need to manage the case.
4. Refine with AI-Assisted Iteration
This is where “vibe-coding” becomes powerful. You guide the AI with intent and clinical insight; the AI handles the syntax.
- “Refactor this messy function.”
- “Make this mobile-friendly for patients.”
- “Add a graph for blood sugar trends.”
5. Deploy, Test, Improve
Ship your first version early. It doesn’t need to be polished; it just needs to work. Doctors learn technology like we learn procedures: See one, do one, teach one. Start supervised (with AI), develop muscle memory, and then scale.
Why This Fits the Medical Mindset
- It mirrors medical education: Case → Prototype → Reflection → Improvement.
- It respects your time: You skip the 80% of computer science theory that doesn’t apply to solving clinical problems.
- It leverages clinical reasoning: Algorithms are just clinical pathways. UI workflows are just patient flow diagrams.
Conclusion: The Era of the AI-Assisted Physician
Physicians don’t need to mimic the learning path of 20-year-old CS majors. You have something far more powerful: a patient-centered purpose and a problem-solving brain.
Coding in 2026 is not about memorizing syntax. It’s about co-creating with AI and developing applications that actually improve patient care.
This is the path. This is how doctors become builders.
Dr. Chukwuma I. Onyeije is a Maternal-Fetal Medicine specialist and the founder of Doctors Who Code. He builds AI-driven tools for healthcare, including Preconception Counseling Co-Pilots and diabetes trackers.