vida: extract claims from 2026-04-21-praim-mammography-optional-use-nature-medicine

- Source: inbox/queue/2026-04-21-praim-mammography-optional-use-nature-medicine.md
- Domain: health
- Claims: 1, Entities: 0
- Enrichments: 1
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>

domains/health/optional-use-ai-deployment-preserves-independent-clinical-judgment-preventing-automation-bias-pathway.md

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+---
+type: claim
+domain: health
+description: PRAIM study's design allowed radiologists to voluntarily choose whether to consult AI after making their own primary read, potentially interrupting the deskilling pathway by preserving active clinical judgment for every case
+confidence: experimental
+source: PRAIM Study, Nature Medicine, January 2025
+created: 2026-04-21
+title: Optional-use AI deployment where clinicians form independent judgment before consulting AI may structurally prevent automation bias and deskilling mechanisms observed in mandatory-use systems
+agent: vida
+scope: structural
+sourcer: Nature Medicine
+challenges: ["human-in-the-loop-clinical-ai-degrades-to-worse-than-AI-alone-because-physicians-both-de-skill-from-reliance-and-introduce-errors-when-overriding-correct-outputs", "automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output"]
+related: ["human-in-the-loop-clinical-ai-degrades-to-worse-than-AI-alone-because-physicians-both-de-skill-from-reliance-and-introduce-errors-when-overriding-correct-outputs", "automation-bias-in-medicine-increases-false-positives-through-anchoring-on-ai-output", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling"]
+---
+
+# Optional-use AI deployment where clinicians form independent judgment before consulting AI may structurally prevent automation bias and deskilling mechanisms observed in mandatory-use systems
+
+The PRAIM study deployed AI mammography screening across 12 German sites with 463,094 women and 119 radiologists using an optional-use design: radiologists made their own primary read first, then voluntarily chose whether to consult AI. This design achieved a 17.6% increase in cancer detection (6.7 vs 5.7 per 1,000 screened) with no increase in recall rate. The structural argument is that optional-use deployment may prevent deskilling because it requires radiologists to exercise active clinical judgment for EVERY case regardless of AI use, positioning AI as a second opinion rather than a primary filter. This contrasts with mandatory or default-on AI deployment where clinicians may passively wait for AI output before forming their own judgment—the mechanism for automation bias and deskilling documented in other studies. The PRAIM study did not formally measure skill degradation, so this remains a plausible structural hypothesis rather than proven effect. The design principle is: if automation bias occurs when clinicians defer judgment to AI, then requiring independent judgment formation before AI consultation should interrupt that pathway.