4 university programmes
6 sources
Comparison
AIMOCS compared with Harvard, Stanford, Johns Hopkins and MIT Sloan
These are the programmes a clinician weighs against this one — what each teaches, what you build, and what continues once the course ends.
Harvard
Stanford
Johns Hopkins
MIT Sloan
The table
The programmes, side by side
| Feature | AIMOCS |  Johns Hopkins |  Harvard |  Stanford |  MIT Sloan |
|---|---|---|---|---|---|
| Last content review | v2026.Q3 · reviewed quarterly, logged publicly | Cohort of 22 Aug 2026 [1] | Runs as a live 3-day programme [2] | CME release 10 Aug 2023, expires 10 Aug 2026 [3a][3b] | 6-week cohort course [4] |
| Generative + agentic AI | Core to two of four tracks: LLMs end-to-end, agentic AI engineering, scribes, golden-set evals, MCP, EHR agents, governance — plus a dedicated Working with Claude course | Curriculum runs from LLMs to goal-directed agentic AI, plus an n8n masterclass [1] | Curriculum covers generative AI; hands-on sessions are demonstrations [2] | No agentic-AI module in the published syllabus; core CME content released Aug 2023 [3][3a] | Published syllabus contains no generative or agentic modules [4] |
| What you build | One artifact per module — 16 total, from a department AI explainer to a capstone pilot proposal | Guided projects + masterclass build [1] | Demonstrations only [2] | Recorded exercises + capstone [3] | Case discussions and a capstone assignment [4] |
| What continues after | Research briefing, author conversations, case-study library, quarterly curriculum refresh | Programme ends at certificate [1] | Programme ends at certificate; alumni events vary [2] | Programme ends at certificate [3] | Programme ends at certificate [4] |
| Format & time commitment | Self-paced tracks, each module ending in one artifact; built around clinical schedules | Online certificate programme, cohort-based [1] | Live 3-day programme [2] | Self-paced specialization [3] | 6 weeks, ~6–8 h per week [4] |
| Prerequisites | None — tracks start from the reader’s actual starting point | None stated [1] | None stated; aimed at practising clinicians [2] | None stated for the specialization [3] | None stated [4] |
- [1] Johns Hopkins — AI in Healthcare Certificate Program
- [2] Harvard Medical School — “AI in Clinical Medicine”
- [3] Stanford — AI in Healthcare Specialization (Coursera)
- [3a] Stanford — Fundamental Machine Learning for Healthcare (CME original release 10 Aug 2023, expiry 10 Aug 2026)
- [3b] Stanford — Evaluations of AI Applications in Healthcare (same CME window)
- [4] MIT Sloan — Artificial Intelligence in Health Care (online short course)
The honest half
Scale
The other side
Where these programmes are ahead of us
Stanford’s specialization has enrolled more than 85,000 learners, and each of these institutions carries a name that needs no explanation on a CV. If an institutional certificate is what you need this year, their programmes earn it.
AIMOCS was built for what those programmes leave open: staying current after the recording is made, and leaving every module with something your organisation can use.
Judge it yourself
The library is open, so you can check the standard before joining.
Read a few pieces first; join if the standard holds up.
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