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Healthcare AI Learning
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Agentic AI in Healthcare

4 modules · 0 completed · ≈ 4 hours

Start — Module 1

Modules

  • 1. From Copilots to Agents30 min
  • 2. Anatomy, Tools & Healthcare Integrations60 min
  • 3. Finding the Work, Autonomy & Oversight55 min
  • 4. Evaluation, Security & Implementation100 min
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From Copilots to Bounded Autonomy

Agentic AI in Healthcare

Four modules on moving from one-shot AI assistance to systems that pursue multi-step goals, use tools, maintain state and take bounded actions inside real healthcare workflows. You learn what 'agentic' means operationally, how to find workflows worth delegating, how tools, permissions and oversight actually constrain what a system can do, how to evaluate a trajectory rather than a paragraph, and how to choose the lowest sufficient autonomy for the value you want. Prior AI foundations help but are not required, and no coding is involved.

Modules completed0 / 4
Module 1
Not started

From Copilots to Agents

What 'agentic' means operationally, how it differs from a generative assistant and from deterministic automation, the agent loop, and how to choose the lowest autonomy a healthcare workflow actually needs.

30 minAssistant vs automation vs agent · The agent loop · Tools, actions and state
Open
Module 2
Not started

Anatomy, Tools & Healthcare Integrations

Two chapters. First the parts you are actually buying or building: model and orchestrator, instructions and policies, governed knowledge, memory versus workflow state, identity and permissions, tracing, and when a second agent helps. Then how agents act through systems: tool contracts and schemas, read versus write, FHIR and EHR integration at leader level, provenance, least privilege, idempotency and safe failure.

60 minModel & orchestrator · Instructions & policies · Governed knowledge
Open
Module 3
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Finding the Work, Autonomy & Oversight

Two chapters. First decomposing real workflows into tasks, decisions, actions, handoffs and exceptions, then judging structure, reversibility, consequence and value to find bounded candidates. Then designing oversight that holds: approval gates set by action risk, permission scopes, escalation and abstention, stop conditions and kill paths, and how to avoid rubber-stamp review.

55 minWorkflow decomposition · Structured vs ambiguous work · Reversibility & consequence
Open
Module 4
Not started

Evaluation, Security & Implementation

Three chapters. Evaluating a trajectory rather than a paragraph: task success, tool selection, action accuracy, recovery, latency, cost and drift. The threat surface that appears once a system can act: prompt injection, excessive agency, exfiltration, memory poisoning, the safety case, audit trail and regulatory routing by intended use. And getting to a running service: sandboxed starts, shadow mode, pilot gates, operating model, sourcing, TCO, incidents and decommissioning.

100 minTrajectory vs output · Task success & action accuracy · Tool selection & arguments
Open

Healthcare AI Learning — structured training on AI in healthcare. Your progress, notes and capstone text are stored in this browser only.

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