Trust & factuality bundle: RAG grounding, uncertainty + error-rate display, compliance docs #59

@Liohtml

Part of #51. Three closely-related trust/factuality improvements grouped as one
tracking issue (can be split into sub-PRs).

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D. Compliance & positioning documentation

Document the intended-use boundary so contributors and users understand what
MedCheck is and isn't — and so the project stays on the safe side of the
medical-device line (see epic #51).

Research basis (verified against primary sources):

• US: a tool that analyzes a medical image fails FDA Cures-Act CDS Criterion 1,
  and a patient-facing tool fails Criterion 3 → it is, on its face, a regulated
  SaMD. The CDS exemption is categorically unavailable to image-analysis software.
• EU: MDR Rule 11 / MDCG 2019-11 — diagnostic-information software is ≥ Class
  IIa, imaging typically IIb; no CDS carve-out.
• A "not a medical device / educational only" disclaimer does not by itself
  exempt software — intended use (claims, labeling, functionality) governs.

Deliverables:

• docs/intended-use.md: explicit intended use + dual positioning (research/
  developer track vs. patient-education track), and the "do / don't" boundary
  (no autonomous image findings presented to patients as truth; explain
  existing reports + terminology + question prep).
• A model card (docs/model-card.md): models used, data provenance, known
  limitations, hallucination risk, no validation claims.
• Map MedCheck against FUTURE-AI principles (Fairness, Universality,
  Traceability, Usability, Robustness, Explainability) and note gaps.
• Strengthen the README/disclaimer wording accordingly.

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E. RAG grounding for factuality (Tier 0 — no training)

Reduce Vision-LLM hallucination by retrieving relevant reference material (curated
finding descriptions, anatomy templates, normal-vs-abnormal exemplars) and
grounding the prompt — no model training required. Research shows training-free
RAG (LaB-RAG, RadioRAG) is competitive and is the best risk/reward first step for a
small team; even SOTA report-generation models hallucinate, so grounding + human
review is mandatory.

Deliverables:

• Optional retrieval over a curated, license-clean reference corpus (e.g. the
  anatomy templates already in prompts/anatomy/, expandable).
• Inject retrieved context into vision_analysis.build_prompt(); cite which
  references were used in the output (traceability).
• Config to enable/disable; lazy deps for any vector store (or a simple in-repo
  embedding/BM25 to avoid heavy deps initially).
• Tests: retrieval returns expected refs; prompt includes grounding; disabled = no-op.

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F. Uncertainty + error-rate display

The evidence-based mitigation for patient harm (false reassurance/alarm) is to
always pair any AI output with its uncertainty and error-rate context. A
controlled study (npj Digital Medicine 2025) found disclosing error rates
measurably reduced harm from patient-facing AI imaging output.

Deliverables:

• Surface per-finding confidence prominently and consistently in all report
  renderers (already on StructureFinding).
• Add standard, honest limitations + error-rate context blocks to reports
  ("AI image analysis can miss findings or flag things that aren't there; this
  is not a diagnosis").
• In the patient report mode (Add patient-friendly plain-language report mode #56 ), present uncertainty in plain language and
  never imply certainty the model doesn't have.
• Feed confidence/limitations into the validator (Add findings cross-validation pipeline step (LLM findings vs. ML signals) #52 ) and reconciliation (Add report reconciliation: compare official radiologist report against image analysis #58 )
  framing.

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Acceptance criteria (bundle)

• D: intended-use + model-card docs merged; README disclaimer strengthened.
• E: optional RAG grounding wired into prompts with citation/traceability.
• F: uncertainty + error-rate context shown consistently across report modes.
• Each sub-item independently testable; all additive (defaults unchanged).

Effort

D ~0.5 day · E ~1–2 days · F ~0.5 day. Light deps.

References (session research)

FDA CDS Final Guidance (Criteria 1 & 3); EU MDR Rule 11 / MDCG 2019-11; FDA General
Wellness guidance; FUTURE-AI (BMJ 2025); WHO AI-for-health ethics (2021/2024); npj
Digital Medicine 2025 (error-rate disclosure); LaB-RAG / RadioRAG.