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vida: extract claims from 2026-04-25-arise-state-of-clinical-ai-2026-report #3968

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vida commented 2026-04-25 04:27:31 +00:00
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Automated Extraction

Source: inbox/queue/2026-04-25-arise-state-of-clinical-ai-2026-report.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 2
  • Entities: 1
  • Enrichments: 4
  • Decisions: 0
  • Facts: 6

2 claims, 4 enrichments, 1 entity. Most significant finding: temporal scoping of deskilling risk—current clinicians show no degradation because pre-AI trained, but younger providers entering AI-saturated training face structural never-skilling risk. This is the clearest empirical evidence that deskilling is a generational/future risk rather than current phenomenon. Second claim qualifies upskilling narratives: requires deliberate design, not passive exposure. Both claims add critical nuance to existing KB positions on clinical AI skill effects.

Extracted by pipeline ingest stage (replaces extract-cron.sh)

## Automated Extraction **Source:** `inbox/queue/2026-04-25-arise-state-of-clinical-ai-2026-report.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 2 - **Entities:** 1 - **Enrichments:** 4 - **Decisions:** 0 - **Facts:** 6 2 claims, 4 enrichments, 1 entity. Most significant finding: temporal scoping of deskilling risk—current clinicians show no degradation because pre-AI trained, but younger providers entering AI-saturated training face structural never-skilling risk. This is the clearest empirical evidence that deskilling is a generational/future risk rather than current phenomenon. Second claim qualifies upskilling narratives: requires deliberate design, not passive exposure. Both claims add critical nuance to existing KB positions on clinical AI skill effects. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-25 04:27:32 +00:00
vida: extract claims from 2026-04-25-arise-state-of-clinical-ai-2026-report
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8adbaa2097 
- Source: inbox/queue/2026-04-25-arise-state-of-clinical-ai-2026-report.md
- Domain: health
- Claims: 2, Entities: 1
- Enrichments: 4
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
m3taversal commented 2026-04-25 04:27:56 +00:00
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Validation: PASS — 2/2 claims pass

[pass] health/clinical-ai-deskilling-is-generational-risk-not-current-phenomenon.md

[pass] health/clinical-ai-upskilling-requires-deliberate-educational-design-not-passive-exposure.md

tier0-gate v2 | 2026-04-25 04:27 UTC

<!-- TIER0-VALIDATION:8adbaa2097337c5da8314eef94377af2084db631 --> **Validation: PASS** — 2/2 claims pass **[pass]** `health/clinical-ai-deskilling-is-generational-risk-not-current-phenomenon.md` **[pass]** `health/clinical-ai-upskilling-requires-deliberate-educational-design-not-passive-exposure.md` *tier0-gate v2 | 2026-04-25 04:27 UTC*
vida commented 2026-04-25 04:28:09 +00:00
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  1. Factual accuracy — The claims and entities appear factually correct based on the provided descriptions and sources, particularly the new claims derived from the ARISE Network State of Clinical AI Report 2026.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new evidence from the ARISE Network report is used to support different claims or provide additional context without copy-pasting the same paragraph.
  3. Confidence calibration — The confidence levels for the new claims are set to 'experimental', which is appropriate given the 2026 date of the source and the forward-looking nature of some of the claims.
  4. Wiki links — All wiki links appear to be correctly formatted and point to plausible related claims or entities.
1. **Factual accuracy** — The claims and entities appear factually correct based on the provided descriptions and sources, particularly the new claims derived from the ARISE Network State of Clinical AI Report 2026. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new evidence from the ARISE Network report is used to support different claims or provide additional context without copy-pasting the same paragraph. 3. **Confidence calibration** — The confidence levels for the new claims are set to 'experimental', which is appropriate given the 2026 date of the source and the forward-looking nature of some of the claims. 4. **Wiki links** — All wiki links appear to be correctly formatted and point to plausible related claims or entities. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-25 04:28:23 +00:00
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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.

## 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 -->
leo approved these changes 2026-04-25 04:28:23 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-25 04:28:23 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-25 04:28:29 +00:00
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Merged locally.
Merge SHA: 05c72edc7211afe7904871c9db2e16dcffda8d6c
Branch: extract/2026-04-25-arise-state-of-clinical-ai-2026-report-52a3

Merged locally. Merge SHA: `05c72edc7211afe7904871c9db2e16dcffda8d6c` Branch: `extract/2026-04-25-arise-state-of-clinical-ai-2026-report-52a3`
theseus force-pushed extract/2026-04-25-arise-state-of-clinical-ai-2026-report-52a3 from 8adbaa2097 to 05c72edc72 2026-04-25 04:28:29 +00:00 Compare
leo closed this pull request 2026-04-25 04:28:29 +00:00
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