Medical AI Education

The Language of
Medical AI, Decoded

Plain-language explanations of AI concepts for physicians, basic science researchers, and clinical researchers — tailored to your role.

RP
30+
Terms
3
Personas
5
Clusters
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Browse the Full Lexicon

30+ terms with role-specific explanations for physicians, scientists, and researchers

How to use the lexicon

1
Click any term on the left to see its definition
2
Then choose your role tab to get a tailored explanation
3
Tap related terms to explore connected concepts

AI Terms

👆 Click any highlighted term below

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Select a term to begin

Choose any term from the list on the left to explore its definition

← Choose a term on the left
Tap a role tab below — each gives you a different angle on this concept

🩺 For Physicians

🔬 For Basic Science Researchers

📊 For Clinical Researchers

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Suggested Term Clusters

Click any cluster to filter the lexicon to related terms

✍️
Prompt Mastery
prompt engineering · system prompt · few-shot · chain-of-thought · temperature · sampling · style transfer
7
🧠
Model Fundamentals
tokenization · embeddings · latent space · attention · residual streams · scaling laws · hallucinations
7
⚙️
Practical Use Cases
RAG · agents · quantization · LoRA · diffusion models · multimodal · data centers
7
🛡️
Safety & Evaluation
hallucinations · RLHF · guardrails · prompt injection · bias · data privacy · emergent abilities
7
🔄
Workflow & Governance
human-in-the-loop · RAG workflows · agents · orchestration · governance · evaluation frameworks
6

Learn at Your Own Pace

Assess your fluency, then deepen it with structured video learning

🎯

MedAI Fluency Studio

A short guided self-assessment to understand your current AI fluency and plan practical next steps. Perfect for faculty, residents, fellows, and staff.

🎬

MedAI MicroModules

15 bite-sized videos (3–8 min each) walking through every core concept — perfect for busy schedules. Watch in order or jump to your focus areas.

Vetting AI Tools

A practical framework for evaluating medical AI tools before adoption — covering evidence quality, bias, transparency, and institutional governance.