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Clinical AI Liability Literacy, Disclosure Confidence, and Adoption Readiness Among Romanian Healthcare Professionals: An Exploratory Single-Center Cross-Sectional Study

Year: 2026

Type: article

Abstract: BACKGROUND AND OBJECTIVES: Errors generated by clinical artificial intelligence (AI) can obscure responsibility and complicate disclosure and incident reporting. This exploratory study examined AI-error liability literacy and its behavioral correlates among Romanian healthcare professionals. MATERIALS AND METHODS: An exploratory, single-center cross-sectional survey was conducted from February to May 2026 among 109 clinical, managerial, legal/compliance, and IT/data professionals. No a priori sample-size calculation was performed; only moderate or larger effects were detectable. Participants completed a 20-item AI Error Liability Literacy Index and five-point behavioral measures. Analyses included Spearman correlations, k-means clustering, logistic regression, and covariate-adjusted mediation, with Firth penalization, role-adjusted, use-stratified, and competing-model sensitivity analyses, and bootstrap validation. RESULTS: Mean liability literacy was 11.6 ± 4.2/20. Familiarity was higher for the General Data Protection Regulation (GDPR) (67.9%) than for the EU AI Act (24.8%). Literacy correlated with disclosure confidence (ρ = 0.49; 95% CI [0.33, 0.62]), an association that was attenuated but persisted after adjustment for professional role (partial ρ = 0.41), which correlated with adoption intention (ρ = 0.38), as did trust (ρ = 0.43); defensive stance correlated negatively (ρ = -0.31). Three exploratory profiles were identified (bootstrap Jaccard 0.71-0.82) that also differed on variables not used in clustering, and an indirect statistical association between literacy and adoption intention through disclosure confidence was observed (indirect effect 0.12; 95% CI [0.04, 0.22]), although concurrent measurement precludes causal or temporal interpretation. Under penalization, associations for literacy, trust, and disclosure confidence persisted; defensive stance and prior training did not. CONCLUSIONS: In this exploratory sample, adoption readiness was associated with regulatory knowledge, disclosure confidence, and calibrated trust. These hypothesis-generating findings require confirmation in larger, longitudinal studies before informing practice, and the appropriate goal is calibrated rather than maximal adoption.

Source: European Journal of Investigation in Health Psychology and Education

DOI: 10.3390/ejihpe16100153

Authors: Daian-Ionel Popa, Ciprian Gîndac, Florina Buleu, Rotaru Roxana Elena, Codrina Mihaela Levai

Institutions: Spitalul Clinic Judeţean de Urgenţă "Pius Brînzeu" Timişoara, Victor Babeș University of Medicine and Pharmacy Timișoara

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,689