Barriers, Facilitators, and Intention to Use AI for Breast Cancer Diagnosis: Mixed Methods Study Among Austrian Physicians With and Without AI Experience

CLAIM (Claim-Specific Citation Network audit, sometimes called CSN) is a forensic method for testing whether a scientific or medical claim's authority is supported by evidence or by citation dynamics. It detects citation bias, amplification, citation diversion, citation transmutation, dead-end citation, and back-door invention.

The ASSAY skill runs a structured, CLAIM-compatible extraction and integrity assessment on an article. Output is a verdict (sound, mixed, flagged, problematic, or cascade), a count of claims extracted, the central key claim, and an integrity note describing the structural read.

This scan restricts ASSAY to peer-reviewed publications and preprint servers. Journalism, opinion pieces, and government documents are evaluated under different frameworks (CAIHL for power and agency; editor's note for context).

ASSAY found the central claims well-supported by the underlying evidence; methodology stands; the integrity-of-citation check raised no structural concerns.

Among Austrian physicians, intention to use AI for breast cancer diagnosis is strongly mediated by prior AI experience; barriers cluster on liability, training, and workflow disruption, while facilitators cluster on perceived diagnostic accuracy and time savings.

Total claims extracted from the article: 7. The key claim is the single most load-bearing assertion the rest of the argument depends on.

Mixed-methods design (quantitative survey + qualitative interviews) is appropriate to the intention-to-use research question. Single-country sample limits generalizability — Austrian physician attitudes are not US physician attitudes — but the structural pattern (prior-experience-mediates-intention) is consistent with the broader technology-acceptance literature. The barrier/facilitator inventory is useful at the deployment-planning level; the causal claim that prior experience drives intention is correlational by design.

Editor's note: The clinician-acceptance rate is the rate limiter on AI breast-cancer-diagnosis deployment, not the model performance. The patient who is screened by an AI-augmented radiology workflow is downstream of a clinician's prior attitude.

Summary: JMIR: Mixed-methods study of Austrian physicians' barriers, facilitators, and intention to use AI for breast cancer diagnosis — stratified by whether the physician has prior AI experience — with implications for clinician-side adoption modeling.

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