DIRECTORY · OPEN SOURCE
Open-Source Healthcare AI
This section is for teams evaluating private-stack building blocks, not turnkey apps. Use it to compare infrastructure and model building blocks that matter when a hospital wants to run AI inside its own environment instead of sending PHI to a cloud vendor.
What this page is for: understand which open-source components belong in a hospital-owned stack and which ones still need governance, evaluation, and operational support before production use.
What this page is for
Two serving runtimes, one local packaging layer, one local AI gateway, one dedicated vector database, one search platform, one workflow orchestration layer, one speech model, one medical-domain model family, and one additional retrieval platform — enough to compare pilot-friendly and platform-grade building blocks inside a private-stack shortlist.
These profiles help buyers decide what belongs inside a hospital-owned AI platform before they compare application-layer workflows.
The people who need to evaluate residency, safety, and GPU economics together rather than in separate workstreams.
Current profiles
The self-hosted inference layer. Useful when a hospital wants OpenAI-compatible serving for approved open-weight models without routing PHI through a third-party API. Read profile →
A medical-domain Gemma family that is more useful as a hospital-evaluated building block than as a turnkey product. Strong fit for internal pilots with clinical oversight. Read profile →
A lightweight local runtime for approved models when the buyer wants CPU-first pilots, edge-style deployments, or offline demonstrations before larger GPU infrastructure is justified. Read profile →
An open speech-recognition building block for private dictation and ambient-note experiments when the organization wants transcription inside its own clinical boundary. Read profile →
A local model packaging and serving layer for teams that need the fastest path from approved model file to governed workstation or lab pilot inside the hospital boundary. Read profile →
A self-hosted vector database for private medical search and document Q&A stacks when the buyer wants retrieval infrastructure to stay inside a hospital-controlled environment. Read profile →
A self-hosted search platform for teams that need keyword, filtered, and vector-aware retrieval over governed hospital content without outsourcing the search layer. Read profile →
An orchestration layer for retrieval-backed assistants when the buyer wants to prototype and govern question-answering workflows on top of local models and private search. Read profile →
An open local AI gateway for hospitals that want OpenAI-style endpoints, model routing, and air-gapped pilots without committing immediately to a larger centralized serving stack. Read profile →
A private vector retrieval layer for teams comparing how to store embeddings, tune recall, and scale document-grounded assistants inside a hospital-controlled environment. Read profile →
How to read this section of the directory
- checkOpen source is not deployment-ready by default. The page should tell you what the component is good for, what governance it still needs, and which buyer constraints it actually solves.
- checkInference layers and models are different buying decisions. vLLM helps you run approved models privately; MedGemma is a candidate model family to evaluate inside that stack.
- checkClinical safety depends on evaluation. The point of these profiles is to shorten the shortlist for a pilot, not to imply unsupervised production use.
Where Moneli Automation fits
Moneli Automation uses profiles like these to turn a vague "we want local AI" request into a specific architecture: which workflows need citations, which models stay in-scope, which GPUs are enough for the pilot, and which review loops have to be present before any clinician touches the output.
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