Claim LD03-SL-C23 (v1.0)
In a field study of three radiology departments in a major US hospital using diagnostic AI, only one department consistently incorporated AI results into professionals' final judgements; in the others, opaque AI results that diverged from experts' own views were often not engaged with.
Provenance and status
- Claim type
- FACT (as printed: “FACT”)
- Publication status
- ELIGIBLE (QUALIFIED) (as printed: “ELIGIBLE WITH QUALIFIER”)
- Verification state
- CONTENT_VERIFIED_PRIMARY (ABSTRACT_ONLY) (as printed: “CONTENT VERIFIED ABSTRACT”; verified by: UNKNOWN)
- Does not license
- Any finding about generative AI; effects on diagnostic accuracy
- Source (field 6, as printed)
- Lebovitz, Lifshitz-Assaf & Levina, “To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis,” Organization Science 33(1), 2022
- Source type (field 7)
- PEER_REVIEWED_JOURNAL_ARTICLE (as printed: “Peer-reviewed journal article”)
- Institutional position
- Independent academic
- Maturity (claim evidence / publication maturity)
- MATURE (as printed: “MATURE”)
- Recorded date
- 2026-10-15
- Currentness / correction
- This is the current version. No correction or concern relation is recorded on its Sources.
Version
Pinned version v1.0 (entry LD03-SL-C23--v1.0) — this page never floats to newer evidence.
This pinned version is also the current version.
Pinned Sources (field 6, version-specific)
- “Lebovitz, Lifshitz-Assaf & Levina, “To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis,” Organization Science 33(1), 2022” → SRC-SL-LEBOVITZ-LIFSHITZ-ASSAF-LEVINA-ORGANIZATION-SCIENCE-2022--v1.0 — Lebovitz, Lifshitz-Assaf & Levina, “To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis,” Organization Science 33(1), 2022; class PEER_REVIEWED_JOURNAL_ARTICLE (explicit public provenance pin)
All 25 canonical Claim fields (raw values and normalized values)
| # | Canonical field | Canonical value (verbatim) | Normalized (Claim Value Vocabulary v1.0.2) |
|---|---|---|---|
| 1 | CLAIM ID | LD03-SL-C23 | — |
| 2 | PUBLIC-FACING CLAIM | In a field study of three radiology departments in a major US hospital using diagnostic AI, only one department consistently incorporated AI results into professionals' final judgements; in the others, opaque AI results that diverged from experts' own views were often not engaged with. | — |
| 3 | CLAIM TYPE | FACT | base_types: FACT |
| 4 | RESEARCH STRAND | E — Organizational practice and decoupling | — |
| 5 | MATURITY | MATURE | states: MATURE |
| 6 | SOURCE | Lebovitz, Lifshitz-Assaf & Levina, “To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis,” Organization Science 33(1), 2022 | source_ids: SRC-SL-LEBOVITZ-LIFSHITZ-ASSAF-LEVINA-ORGANIZATION-SCIENCE-2022 |
| 7 | SOURCE TYPE | Peer-reviewed journal article | classes: PEER_REVIEWED_JOURNAL_ARTICLE |
| 8 | INSTITUTIONAL POSITION | Independent academic | — |
| 9 | SCOPE | Three radiology departments in one US hospital | — |
| 10 | DESIGN | Qualitative field study | — |
| 11 | SAMPLE / DATASET | Three departments | — |
| 12 | MODEL / TOOL / VERSION | Diagnostic AI (pre-generative AI) | — |
| 13 | COMPARATOR | Departments compared with one another | — |
| 14 | MAIN RESULT | Only one of three departments achieved 'engaged augmentation', consistently incorporating AI results into final judgements | — |
| 15 | INFERENTIAL TARGET | How the same AI is absorbed differently by different professional units | — |
| 16 | DOES NOT LICENSE | Any finding about generative AI; effects on diagnostic accuracy | — |
| 17 | VERIFICATION STATE | CONTENT VERIFIED ABSTRACT | state: CONTENT_VERIFIED_PRIMARY · qualifiers: ABSTRACT_ONLY · verified_by: UNKNOWN |
| 18 | LIMITATIONS | Checked against the official abstract only, read in the Crossref record on 15 October 2026; one hospital; diagnostic AI, so an analogue | — |
| 19 | CONTRADICTORY / SUPPORTING SOURCES | Consistent with the practice view that the same tool is enacted differently across settings | — |
| 20 | GEOGRAPHY / LANGUAGE | US / English | — |
| 21 | DISTRIBUTIONAL LEDGER | Not analysed in this claim | — |
| 22 | R01 / FIELD-MAP LINKS | LD-03, D2 | — |
| 23 | WHAT WOULD CHANGE THIS CLAIM? | Generative-AI field studies finding uniform absorption across units | — |
| 24 | PUBLICATION STATUS | ELIGIBLE WITH QUALIFIER | state: ELIGIBLE · qualifiers: QUALIFIED |
| 25 | RECORDED DATE | 2026-10-15 | — |
Field 22 references resolved against Field Map v0.2: LD-03 (LIVING_DEBATE); D2 (DOMAIN)
Cited by Living Debate versions
Research-of-record provenance
Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 ee96da178497e6bf0bd4fcb1adc32554c5667200408aa4187e552a1d225333ed.
Repository-authored inputs (not Drive exports)
- SocioLens claim — LD03-SL-C23 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-15; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-03 debate; a new Claim, first version, written from the project's LD-03 source notes, checked against full texts or official abstracts. Block “ENTRY 23 — LD03-SL-C23” SHA-256
c23dc5b2f9fa965ce7352ddb48c22e643e16175f3fefea13dc10e7e63c4833a5; fixturefixtures/authored/claims/LD03-SL-C23--v1.0.
Builder decisions (not Drive data)
- VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD03-SL-C23 v1.0); the first version of a new Claim of the LD-03 debate
- CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD03-SL-C23 v1.0" (Project Lead (Claude, AI), 2026-10-15); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-03 debate; a new Claim, first version, written from the project's LD-03 source notes, checked against full texts or official abstracts
- FIELD_6_SOURCE_REFERENCE = SRC-SL-LEBOVITZ-LIFSHITZ-ASSAF-LEVINA-ORGANIZATION-SCIENCE-2022--v1.0 — basis: field 6 of ENTRY 23 — LD03-SL-C23 cites "Lebovitz, Lifshitz-Assaf & Levina, “To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis,” Organization Science 33(1), 2022"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
- SOURCE_PROVENANCE_PIN = SRC-SL-LEBOVITZ-LIFSHITZ-ASSAF-LEVINA-ORGANIZATION-SCIENCE-2022--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Lebovitz, Lifshitz-Assaf & Levina, “To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis,” Organization Science 33(1), 2022".