Building GenAI Applications in Healthcare
6 modules · 0 completed
Modules
- 1. Your First Healthcare GenAI ApplicationAvailable
- 2. Grounding with Retrieval (RAG)Available
- 3. Tools & Function CallingAvailable
- 4. Build a Healthcare AgentAvailable
- 5. Evals for Healthcare GenAIAvailable
- 6. Capstone: Ship a Bounded Healthcare GenAI FeatureAvailable
Also useful with this course
- Visual Guide · FHIR · API · MCP · RAG · LLM
- Visual Guide · How an AI Healthcare Application Fits Together
- Visual Guide · LLM · RAG · Fine-tuning · Tools · Agents
- Visual Guide · From Prompt to Agent
- Visual Guide · AI Evaluation in Healthcare
- Visual Guide · From Healthcare Problem to Scaled AI Solution
How this content was reviewed
AI-assisted authoring was followed by structural checks, cross-model AI review and synthetic-user testing. No human clinical, regulatory, scientific or legal expert validation is currently claimed.
- AI-assisted authoring — completed
- Deterministic structural QA — completed
- Cross-model AI review — completed
- Synthetic-user testing — completed
- Human domain-expert review — not yet completed
Status checked 19 September 2026. Primary regulatory and legal summaries checked 25 August–10 September 2026.
Hands-on labs, not lectures
Building GenAI Applications in Healthcare
A build track. You assemble real request bodies, design JSON schemas, run simulated model calls, wire in retrieval from a governed knowledge base, declare and validate tools, compose a bounded agent loop, evaluate it against a fixed case set, and finish by taking one workflow end to end.
Your First Healthcare GenAI Application
Assemble an LLM API request that turns a synthetic consultation transcript into validated structured clinical output — then break it, diagnose it and fix it.
Grounding with Retrieval (RAG)
Attach a governed knowledge source to the request so answers cite something other than the model's memory — then evaluate retrieval and answer as two separate layers.
Tools & Function Calling
Declare tools, read a model's proposed call as an untrusted request, validate its arguments, gate every write behind human approval and handle failures without confusing intent with authorisation.
Build a Healthcare Agent
Compose instruction, tools, explicit state, limits and stop conditions into a bounded loop — then diagnose a runaway trace and fix it in policy rather than in prompt wording.
Evals for Healthcare GenAI
Build a small evaluation set with explicit expectations, split automated from human checks, run a baseline and a variant, read the failure slices and turn the result into a release decision.
Capstone: Ship a Bounded Healthcare GenAI Feature
Take one synthetic workflow end to end — scope, contract, grounding, tools, gates, validation, evaluation, release decision and handover — and defend every boundary.
Also useful with this course
- Visual Guide · FHIR · API · MCP · RAG · LLM
- Visual Guide · How an AI Healthcare Application Fits Together
- Visual Guide · LLM · RAG · Fine-tuning · Tools · Agents
- Visual Guide · From Prompt to Agent
- Visual Guide · AI Evaluation in Healthcare
- Visual Guide · From Healthcare Problem to Scaled AI Solution
How this content was reviewed
AI-assisted authoring was followed by structural checks, cross-model AI review and synthetic-user testing. No human clinical, regulatory, scientific or legal expert validation is currently claimed.
- AI-assisted authoring — completed
- Deterministic structural QA — completed
- Cross-model AI review — completed
- Synthetic-user testing — completed
- Human domain-expert review — not yet completed
Status checked 19 September 2026. Primary regulatory and legal summaries checked 25 August–10 September 2026.