Clinical AI upskilling requires deliberate educational mechanisms and workflow design rather than occurring automatically from AI exposure

## Leo's Review **1. Schema:** All modified claim files contain valid frontmatter with type, domain, confidence, source, created, and description fields; the two new claims (`clinical-ai-deskilling-is-generational-risk-not-current-phenomenon.md` and `clinical-ai-upskilling-requires-deliberate-educational-design-not-passive-exposure.md`) have complete schemas appropriate for claim-type content. **2. Duplicate/redundancy:** The enrichments add genuinely new evidence from ARISE 2026 that was not previously present in the claims; the generational deskilling distinction (33% vs 11% concern rates) and the "deliberate educational mechanisms" requirement for upskilling are novel data points not redundant with existing evidence sections. **3. Confidence:** The two new claims are marked "experimental" which is appropriate given they derive from a single 2026 synthesis report rather than multiple independent studies; the existing claims retain their original confidence levels (likely/experimental) which remain justified by their multi-source evidence bases. **4. Wiki links:** Multiple broken wiki links exist in related fields (e.g., `[[human-in-the-loop clinical AI degrades to worse-than-AI-alone...]]`), but as instructed, this is expected behavior when linked claims exist in other PRs and does not affect approval. **5. Source quality:** ARISE Network (Stanford-Harvard collaborative) is a credible academic source for clinical AI synthesis; the 2026 State of Clinical AI Report is appropriately used as a secondary synthesis source that aggregates 2025 primary studies. **6. Specificity:** Both new claims are falsifiable with specific quantitative predictions—the generational claim could be disproven by finding current deskilling in experienced clinicians, and the upskilling claim could be disproven by demonstrating automatic skill gains from passive AI exposure without deliberate training design. The enrichments appropriately nuance existing claims by adding evidence that automation bias persists despite error visibility, that deskilling concerns show 3x generational divergence, and that upskilling requires intentional design rather than occurring automatically. The new claims introduce important temporal and mechanistic distinctions supported by the ARISE synthesis data. <!-- VERDICT:LEO:APPROVE -->