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Infrastructure for clinical-adjacent AI tools — compliance is on you.

Healthcare AI backends need async processing, auditable data storage, and careful access control. FastAPI AI Kit provides the architectural pieces (async Postgres logging, per-key access control, background jobs) that make it easier to build a compliant system on top of — it does not itself provide HIPAA certification or a signed BAA.

FastAPIPostgreSQLCeleryOpenAI / AnthropicRedis

The usual pain points

✕Meeting HIPAA/data-residency requirements for storage and processing
✕Maintaining an audit trail of who accessed what data and when
✕Processing clinical documents (notes, transcripts) asynchronously at scale
✕Restricting access to PHI-adjacent data by role or API key

How the kit solves them

SQLAlchemy async ORM with structured logging supports building audit trails on top
Per-API-key access control restricts which collections/endpoints a key can reach
Celery background jobs process documents without holding open HTTP connections
Note: HIPAA compliance depends on your hosting provider's BAA, your own access controls, and your data handling practices — the kit does not certify compliance on your behalf

Example implementation

main.py

@router.post("/v1/clinical/summarize-note")
@require_api_key(tier=["clinician"])
async def summarize_note(body: ClinicalNoteRequest, key: APIKey = Depends(get_api_key)):
    await audit_log.record(actor=key.id, action="summarize_note", resource_id=body.note_id)
    summary = await llm.chat(messages=build_clinical_prompt(body), track_tokens=True)
    return {"summary": summary.content}
    # Compliance note: ensure your LLM provider agreement and hosting meet your BAA requirements

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