You Are Past the Starting Line
You Have the Foundation
If you made it through the guided path, you understand what AI is, how to use it safely, and where it fits in your workflow. Everything from here is about going deeper in the areas that matter most to your work.
If you've read through the earlier articles in this path, you now understand what AI is, how to use it, and how to use it responsibly. You've tried summarizing papers, reasoning through clinical scenarios, and drafting professional communication. You know the difference between general-purpose models and specialized tools, and you have a sense of the current model landscape.
That puts you ahead of the vast majority of clinicians right now. But the basics are just the foundation.
Become a Power User: Prompt Engineering
The difference between a casual AI user and a power user is almost entirely about how they frame their prompts. Our Prompt Engineering collection goes deeper than the basics.
What you'll learn:
- Advanced techniques: chain-of-thought prompting, few-shot examples, system-level instructions
- Building reusable prompt templates for tasks you do repeatedly (clinic notes, teaching materials, review articles)
- Handling model limitations: when to push back, when to switch models, when to break a problem into smaller pieces
- The art of the follow-up: why the third message in a conversation is often the most valuable
If you invest time in one thing, invest it here. Better prompting translates directly to better output from every model you use.
See What Is Being Built: Clinical AI
The consumer AI tools you've been learning about are just one side of the coin. There's an entire world of AI tools being built specifically for clinical practice.
| Clinical AI Type | What It Does | Example Tools |
|---|---|---|
| Auto-contouring | Generate treatment contours in seconds | MVision, Limbus AI, RaySearch |
| Dose prediction | Estimate achievable plan quality before planning | PlanIQ, RapidPlan |
| Outcome prediction | Integrate patient, tumor, treatment variables | Various research platforms |
| Adaptive therapy | Use AI to adjust treatment in real time | Ethos, MRIdian AI modules |
| Clinical documentation | NLP tools for clinical notes | Nuance DAX, Abridge |
Our Clinical AI collection covers what exists, what's coming, and—critically—how to evaluate these tools with the same rigor you apply to any new clinical technology. Understanding the AI foundations you've built here will make you a far more informed evaluator of clinical AI products.
For the Ambitious: Learn to Build
If you find yourself thinking "I wish there was a tool that did X"—you might be the person to build it. You don't need a computer science degree. Modern AI tools have lowered the barrier to building useful applications dramatically.
Our Coding collection is designed for clinicians who want to go from zero to building:
- Python fundamentals with a clinical lens
- Working with clinical datasets programmatically
- Building simple AI-powered tools and prototypes
- Understanding enough about AI architectures to collaborate effectively with engineers
This isn't for everyone, and it doesn't need to be. But if you have any inclination toward building, the tools have never been more accessible.
The Frontier: What Is Coming Next
The Common Thread
AI is moving from answering questions to completing tasks. From tool to collaborator. The clinicians who understand this technology now will be the ones who shape how it enters practice.
The AI landscape is moving fast enough that anything I write about "the future" will feel dated within months. But the broad directions are clear.
Agents are AI systems that don't just generate text but take actions. They can search the web, read documents, run code, and execute multi-step workflows autonomously. Today's agents are early but functional. Tomorrow's will be transformative—imagine describing a research question and having an agent conduct a preliminary literature review, extract relevant data, and draft an initial summary, all while you see patients.
Deep research tools are already here in early form. They spend minutes instead of seconds on your question, conducting multi-step investigations across sources and producing comprehensive reports. For complex clinical or research questions, these are qualitatively different from a standard chatbot response.
Reasoning models that think step by step before answering are getting better at complex clinical reasoning, multi-variable decision-making, and problems that require careful logic rather than pattern-matched responses.
Multimodal AI—models that can process images, audio, and video alongside text—will increasingly integrate into clinical workflows. Imagine uploading a treatment plan and asking a model to review it, or showing a model a scan and asking for a structured assessment.
Stay Current
This field moves too fast for static knowledge. What's cutting-edge today is standard tomorrow, and new capabilities emerge monthly.
Follow our newsletter for curated updates on the AI tools and developments that matter for clinicians. We filter the noise so you don't have to track every model release and benchmark.
Final Thought
You don't need to become an AI expert. You need to become a clinician who knows how to leverage AI effectively. Those are very different things.
The expert knows how the transformer architecture works, can fine-tune a model, and debates training methodologies. You don't need any of that. You need to know which tool to use, how to ask good questions, when to trust the output, and when to verify it.
You already have the clinical expertise that makes AI outputs useful rather than dangerous. Now you have the AI literacy to actually use these tools. That combination—clinical judgment plus AI fluency—is what the next decade of medicine will demand.
Keep experimenting. Keep questioning. Keep building on what you've learned here. The tools will keep getting better. Make sure you do too.