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.

SocioLens project,

Claim LD03-SL-C23 (v1.0)

Provenance and status

Claim type: Fact
Publication status: Eligible for publication, with a qualifier
Verification state: Checked against the abstract of the primary source only. Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.
Does not license: Any finding about generative AI; effects on diagnostic accuracy
Limitations: Checked against the official abstract only, read in the Crossref record on 15 October 2026; one hospital; diagnostic AI, so an analogue
Source (as cited): 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: Peer-reviewed journal article (recorded as “Peer-reviewed journal article”)
Institutional position: Independent academic
Maturity of the evidence: Mature evidence
Recorded date: 15 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version 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 (Peer-reviewed journal article)
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Version

This is the current version, v1.0. Earlier versions stay available, and their records never change.

Versions: v1.0 · current

Raw record: every recorded field, with its coded values

All 25 canonical Claim fields, verbatim, with the coded values derived from them. The plain-language labels above are read from these.

Claim type (coded): FACT (as printed: “FACT”)
Publication status (coded): ELIGIBLE (QUALIFIED) (as printed: “ELIGIBLE WITH QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY (ABSTRACT_ONLY)
Source type (coded): PEER_REVIEWED_JOURNAL_ARTICLE
Maturity (coded): MATURE

#	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)

The sources as recorded, with their record IDs and coded source types:

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” → Lebovitz et al. (2022), “To Engage or Not to Engage with…” (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)

Cited by Living Debate versions

Organizational absorption: Why micro gains do not become macro outcomes (v0.1) LD-03

Cite this page

SocioLens project. (2026, October 15). 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 [Claim LD03-SL-C23, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld03-sl-c23/v1.0/

@misc{sociolens_ld03_sl_c23_v1_0,
  author = {{SocioLens project}},
  title = {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.},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld03-sl-c23/v1.0/}},
  note = {Claim LD03-SL-C23, version v1.0}
}

The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.

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; fixture fixtures/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".