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Medical vertical — scientific tutorial

Released in FCC v1.2.0. You are running controlled experiments on LLM behavior in the Medical domain. This tutorial shows how to instrument a scenario with CLEAR+ benchmarks, swap providers via the ai_config scenario override, and measure risk-classification stability across runs.

The Medical pack in one paragraph

The healthcare vertical pack (at src/fcc/data/verticals/healthcare.yaml) contains 11 healthcare personas (v2.1.0) — 5 retrofitted from v1.0 plus 6 v1.2.0 additions for genomic research, clinical imaging (DICOM), telemedicine UX, population health, precision medicine strategy, and medical device software. Headline compliance frameworks: HIPAA, HL7 FHIR R5, ICD-11, IEC 62304, EU MDR, DICOM, IMDRF SaMD.

Focus persona: CIE — Clinical Imaging Engineer (DICOM)

We'll anchor this tutorial on CIE, because it's the one most relevant to the scientific audience in the Medical domain.

from fcc.verticals.registry import VerticalRegistry

reg = VerticalRegistry.from_builtin()
pack = reg.get("healthcare")
persona = next(p for p in pack.personas if p.id == "CIE")

print(persona.name)
print(persona.risk_category or "minimal")
riscear = persona.riscear or {}
print("Archetype:", riscear.get("archetype"))
print("Role:", riscear.get("role"))

Experiment design

You want to answer questions like "does swapping Anthropic for Ollama change how CIE classifies risk?" or "does LiteLLM routing add latency variance I should report?"

The v1.1.0+ ai_config scenario override lets you pin provider/model per scenario without touching the YAML:

# scenarios/healthcare_rct.yaml
scenario_id: HEA-RCT
ai_config:
  provider: litellm
  model: ollama/llama3.2
  temperature: 0.0
  max_tokens: 2000

Then run the CLEAR+ benchmark runner in --mock mode first to get a deterministic baseline:

fcc benchmark run --scenario HEA-RCT --mock --output _output/benchmarks/baseline.json

Then swap to a real provider and compare:

fcc benchmark run --scenario HEA-RCT --output _output/benchmarks/live.json
fcc benchmark compare baseline live

Stable risk classification under model swaps

The AIActClassifier in FCC is deterministic — it doesn't call the LLM. But persona outputs change across providers, so downstream classifiers that inspect outputs may drift.

from fcc.compliance.classifier import AIActClassifier

classifier = AIActClassifier()
# Stable across runs because the override is data-driven, not model-driven:
risk = classifier.classify_persona(persona, vertical_domain="healthcare")
assert risk.value in {"minimal", "limited", "high", "unacceptable"}

This gives you a ground-truth label you can use as a reference in your experiments.

Verify what you did

Run the vertical test suite to make sure your changes didn't break anything:

pytest tests/test_verticals.py -k "healthcare" -v

All scientific-path steps in this tutorial leave your working tree unchanged — the pack YAML is read-only from your perspective. The only state that accumulates is in _output/ (scenario run traces) and docs/model-cards/ (if you regenerated cards).

Next steps