AI in Healthcare
10 modules · 0 completed
Modules
- 1. From AI Idea to Healthcare ImpactAvailable
- 2. Can We Trust the Data?Available
- 3. From Prediction to Clinical UtilityAvailable
- 4. Generative AI in HealthcareAvailable
- 5. RAG, Tools & AI Agents for PractitionersAvailable
- 6. Evaluating Healthcare AIAvailable
- 7. Safety, Human Factors & Responsible AIAvailable
- 8. Strategy, Portfolio & EconomicsAvailable
- 9. Regulation & GovernanceAvailable
- 10. Implementing & Scaling AI in HealthcareAvailable
About this course
The applied next step after the Essentials foundation. Ten modules for healthcare leaders who already understand what AI is and now have to act on it: healthcare data, clinical utility, generative AI and productivity, retrieval and agents, evaluation, safety, strategy and economics, regulation and implementation. Concepts are recalled briefly, not re-taught. The work is assessing evidence, challenging vendors, setting operating points, designing controls, redesigning workflows, deciding what to fund and leading adoption — with a board-ready capstone running through all of them.
Practitioner · 10 released modules
- Who it is for
- Clinical, digital and executive leaders in healthcare and health tech.
- What you will be able to do
- Assess evidence and vendors, decide what to fund, implement safely and monitor what is live.
- Key topics
- Four surfaces of value · Problem → output → decision → action → outcome · Four questions before any model · Actionability & capacity · Value hypothesis · What external results license locally
Also useful with this course
- Tool · Data Readiness Assessment
- Tool · Evidence pack questions
- Tool · AI Claim Analyzer
- Case · A vendor has sent me an evidence pack
- Case · Is this a medical device? Is it high-risk?
- Practice · Diagnostic Metrics Playground
- Practice · Model Evaluation Lab
- Practice · The proposal that should be approved
- Practice · AI Safety Incident
- Practice · Regulatory Classification Lab
- Visual Guide · How an AI Healthcare Application Fits Together
- Visual Guide · The Healthcare Data Landscape
- Visual Guide · AI Evaluation in Healthcare
- Visual Guide · AI Governance 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.
From Models to Real-World Impact
AI in Healthcare
The applied next step after the Essentials foundation. Ten modules for healthcare leaders who already understand what AI is and now have to act on it: healthcare data, clinical utility, generative AI and productivity, retrieval and agents, evaluation, safety, strategy and economics, regulation and implementation. Concepts are recalled briefly, not re-taught. The work is assessing evidence, challenging vendors, setting operating points, designing controls, redesigning workflows, deciding what to fund and leading adoption — with a board-ready capstone running through all of them.
Build your Board Pack as you go — sections unlock with the modules you complete.
Capstone workspaceFrom AI Idea to Healthcare Impact
The bridge from Essentials: where healthcare AI value actually appears, how to read any proposal along the chain from problem to output to decision to action to outcome to value, four questions that screen a proposal out before a business case, actionability under real capacity, and stating a proposal as a falsifiable value hypothesis.
Can We Trust the Data?
Where healthcare data comes from and what it can honestly support: provenance, the prediction moment and time windows, how the outcome was defined and by whom, leakage, missingness as a clinical signal, representativeness and shift, and terminology. Includes a data readiness assessment and an optional technical deep dive.
From Prediction to Clinical Utility
What has to be true for a prediction to improve care: precise intended use, the chain from prediction to decision to action to outcome, the operating point as clinical and operational policy, actionability under capacity, prediction versus treatment-effect claims, and what evidence counts locally.
Generative AI in Healthcare
What changes when a system generates rather than predicts: where generative AI creates value across clinical, administrative, patient-facing and research work, how it fails, prompting as specification, multimodality, version change, and what to inspect before trusting output. Model mechanics are an optional deep dive.
RAG, Tools & AI Agents for Practitioners
Why retrieval exists and what it does not fix, governed knowledge and provenance, two-layer evaluation, tool use, memory and state, choosing an autonomy level with approval gates, containment, and an applied design lab. Search engineering is optional; building agents belongs to the Agentic AI course.
Evaluating Healthcare AI
From questioning a claim to making an evidence decision: which evidence to demand at each layer, what the headline metrics do and do not tell you, calibration, whether a result transfers to your setting, subgroup evidence, red flags in an impressive deck, and the pilot or procurement decision that follows. Technical methodology stays optional.
Safety, Human Factors & Responsible AI
The safety-case chain from failure mode to hazard, harm, control, monitoring signal, stop rule and owner: human factors and meaningful oversight, differential harm, explainability claims, security read as patient safety, and an applied safety review. Failure modes themselves are recalled from earlier modules rather than re-taught.
Strategy, Portfolio & Economics
Opportunity and use-case prioritisation, portfolio choices, buy/build/partner decisions, vendor strategy and due diligence, total cost of ownership, ROI and value realisation, funding and operating-model choices. The central question: what should we pursue, and why?
Regulation & Governance
Which rules actually apply to a proposed AI use, and how to turn that into decisions: AI Act classification and the current timeline, MDR/IVDR and software as a medical device, GDPR, EHDS, and an approval path with named owners rather than a committee maze.
Implementing & Scaling AI in Healthcare
Workflow redesign, pilot design, change management and adoption, stakeholder alignment, integration, go-live gates, monitoring ownership, incident and change management, scaling, and decommissioning as part of the learning loop. The central question: how do we make the chosen AI work safely in practice, and scale it?
About this course
Practitioner · 10 released modules
- Who it is for
- Clinical, digital and executive leaders in healthcare and health tech.
- What you will be able to do
- Assess evidence and vendors, decide what to fund, implement safely and monitor what is live.
- Key topics
- Four surfaces of value · Problem → output → decision → action → outcome · Four questions before any model · Actionability & capacity · Value hypothesis · What external results license locally
Also useful with this course
- Tool · Data Readiness Assessment
- Tool · Evidence pack questions
- Tool · AI Claim Analyzer
- Case · A vendor has sent me an evidence pack
- Case · Is this a medical device? Is it high-risk?
- Practice · Diagnostic Metrics Playground
- Practice · Model Evaluation Lab
- Practice · The proposal that should be approved
- Practice · AI Safety Incident
- Practice · Regulatory Classification Lab
- Visual Guide · How an AI Healthcare Application Fits Together
- Visual Guide · The Healthcare Data Landscape
- Visual Guide · AI Evaluation in Healthcare
- Visual Guide · AI Governance 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.