AI in Healthcare
10 modules · 0 completed · ≈ 15 hours
Modules
- 1. AI Foundations for Healthcare Leaders50 min
- 2. Healthcare Data120 min
- 3. Clinical AI: From Prediction to Clinical Utility85 min
- 4. Generative AI & Healthcare Productivity120 min
- 5. RAG & AI Agents115 min
- 6. Evaluating Healthcare AI85 min
- 7. Safety, Human Factors & Responsible AI70 min
- 8. AI Strategy, Portfolio & Economics95 min
- 9. Regulation & Governance95 min
- 10. Implementing & Scaling AI in Healthcare95 min
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 workspaceAI Foundations for Healthcare Leaders
Practitioner level, building on the Essentials course: target and label design, the prediction moment and which predictors are legitimately available, leakage from process-generated data, the operating point as clinical and operational policy, actionability under real capacity, and stating a proposal as a falsifiable value hypothesis.
Healthcare Data
EHR structures, imaging, notes and wearables; provenance, time windows, informative missingness, dataset shift, target leakage, FHIR versus SNOMED CT, and what the European Health Data Space changes.
Clinical AI: From Prediction to Clinical Utility
Diagnosis, prognosis, risk stratification, monitoring and treatment response; precise intended use, the chain from prediction to decision to action to outcome, threshold trade-offs, causal versus predictive claims, and the clinical evidence that actually counts.
Generative AI & Healthcare Productivity
How large language models actually work, what changes when a system generates rather than predicts, prompting as specification, hallucination forensics, multimodality, and where generative AI creates value and risk across clinical, administrative, patient-facing and research work.
RAG & AI Agents
Why retrieval exists and what it does not fix, the anatomy of a grounded system, provenance and citation discipline, two-layer evaluation, tool calling, agent components, memory and state, the autonomy ladder, agent security and an applied design lab.
Evaluating Healthcare AI
How to judge whether a healthcare AI claim is credible: which evidence to demand at each layer, what the headline metrics do and do not tell you, whether a result transfers to your setting, the red flags in an impressive-looking deck, the evidence ladder from validation to real-world impact, and the questions to ask a vendor or data-science team. Technical methodology is available as optional deep dives.
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.
AI 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?