DETERMINANTS OF ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEMS ADOPTION AMONG MENTAL HEALTH PROFESSIONALS: THE ROLES OF EMPATHY, ACCOUNTABILITY AND TRUST
This article shows how the Technology Acceptance Model and Technology Trust are combined in the Unified Theory of Acceptance and Use of Technology (UTAUT) and how the Perceived Empathy Model (PEM) and Perceived Clarity of Accountability Model (PCA) are extensions of these models.
Artificial intelligence (AI) is increasingly integrated into healthcare to support diagnostics, decision-making, and administrative processes. However, the successful implementation of AI depends not only on technical performance but also on public perceptions of its helpfulness, riskiness, and fairness. This study examines public perceptions of automated decision-making (ADM) in healthcare. Data were drawn from the first wave of an ongoing longitudinal survey panel. The final sample consisted of 3,915 respondents and was analyzed with structural equation modeling. Perceptions of ADM in healthcare as helpful, risky, and fair were treated as the dependent variables. AI literacy, familiarity with different forms of AI, confidence in clinicians'ability to distinguish AI- from human-generated content, use of conversational agents for health information, and use of traditional digital health information sources were included as exogenous. Greater familiarity with different forms of AI, higher confidence in the clinician's ability to recognize AI-generated content, and use of conversational agents for health information were associated with greater perceived helpfulness. Use of conversational agents was associated with lower perceived risk, whereas greater familiarity with AI and greater reliance on traditional health information sources were associated with higher perceived risk. Perceptions of ADM as fair were most strongly predicted by confidence in the clinician's ability, with additional small positive associations with AI familiarity, AI literacy, and use of conversational agents. Public perceptions of ADM in healthcare are shaped by technological familiarity, use of conversational agents, and confidence in human oversight. Overall, ADM's perceived helpfulness and fairness are driven more by trust in healthcare professionals than by trust in the technology itself.
Leonie Westerbeek, Ernesto de Leon, J. V. Weert· 0 citations
A list of eight best practices was created to assist developers with designing AI systems in a way that would reduce the overall risk of harm for users attempting to use their AI for mental health cases.
Joshua Frankenfield, Briana M. Sobel, Barbara Chaparro· Proceedings of the Internati...· 0 citations
Amid the global race toward intelligent healthcare systems, Saudi Arabia stands at a pivotal moment in its digital health transformation. Understanding how prepared healthcare professionals are to adopt artificial intelligence is essential for shaping successful national strategies.
This study aimed to assess healthcare professionals’ attitudes, perceptions, and intentions toward using AI in clinical practice; examine awareness and actual use; identify key predictors of AI adoption based on the Unified Theory of Acceptance and Use of Technology (UTAUT); and explore the mediating role of institutional support.
A cross-sectional survey was conducted among 521 healthcare professionals, including physicians, nurses, administrators, and allied health workers, across Saudi Arabia. The survey assessed awareness, usage, perceived usefulness, ease of use, social influence, facilitating conditions, perceived risks, and the intention to use AI. Data were analyzed using chi-squared tests, multiple regression, and mediation analyses.
Although AI awareness was remarkably high (89.1%) and optimism toward the future was strong (79.0%), only 51.2% of participants reported actual clinical use of AI, A 37.9 percentage-point awareness–use gap. Two factors consistently stood out as powerful drivers of intention: believing that AI is genuinely useful (
B
= 0.491,
p
< .001) and feeling confident in one's ability to use it (
B
= 0.224,
p
< .001). Institutional support played an important but mostly indirect role in shaping intentions by enhancing these two beliefs. Social influence had little effect and was negative for nurses, whereas perceived risk did not significantly deter adoption. Despite structural and ethical challenges, intention to use AI remained high (71.6%), A 20.4 percentage-point gap ahead of actual use, indicating that organizational barriers, rather than individual willingness, remain the primary obstacle to AI integration.
These dynamics provide unique opportunities. With 71.6% of professionals intending to adopt AI, targeted training initiatives, clearer governance frameworks, and organizational support may help facilitate the sustainable integration of AI into healthcare practice. This study offers empirical evidence and a roadmap for transforming enthusiasm into sustainable, safe, and meaningful AI integration, and may support healthcare leaders and policymakers in developing strategies for safe and sustainable AI integration within the Saudi healthcare system.
Advancing AI-enabled healthcare in Saudi Arabia requires investment not only in technology, but also in healthcare professionals’ preparedness and organizational support. Structured training programs, hands-on exposure to AI tools, supportive leadership, adequate infrastructure, and clear data governance frameworks may help healthcare professionals adopt AI more confidently and sustainably within clinical practice.
Yara Alrashed, N. Alsaheil· Frontiers in Health Services· 0 citations
This study demonstrates that extending the Technology Acceptance Model by incorporating nurses' knowledge and attitudes provides a meaningful framework for predicting AI adoption in resource-limited healthcare settings.
S. Sohrabi, Hossein Bonakchi, Rahman Kazemi· BMC Nursing· 0 citations
Background and objectives: Artificial intelligence (AI) is increasingly embedded in public health workflows, yet adoption among practitioners remains uneven and is shaped by knowledge, legal awareness, and operational barriers. This cross-sectional study characterised determinants of AI adoption among healthcare professionals and examined how legal concern moderates the translation of technical knowledge into practical use, at a single Romanian tertiary academic centre. Methods: We surveyed 93 healthcare professionals (physicians, nurses, public health specialists, residents) at the “Pius Brînzeu” Clinical Emergency County Hospital and “Victor Babeș” University of Medicine and Pharmacy Timișoara. Participants were classified as AI adopters or non-adopters. Likert-derived composite scores (0–100; Cronbach’s α 0.79–0.88) quantified knowledge, trust, legal concern, privacy concern, and workflow confidence. Group comparisons used independent-samples t-tests and χ2 tests; associations used Pearson correlation; predictors of adoption and usage intensity were modelled with logistic and multiple linear regression; a two-way ANOVA tested profession-by-training effects. Significance was set at p < 0.05. Benjamini–Hochberg false-discovery-rate correction was applied across the 18 bivariate tests reported in this study, and adjusted q-values are reported alongside unadjusted p-values. Results: Adopters (n = 51) were younger (34.7 ± 7.5 vs. 43.2 ± 8.5 years; p < 0.001) and reported higher knowledge (67.3 vs. 48.6; p < 0.001) and workflow confidence (64.2 vs. 41.9; p < 0.001) but lower legal concern (58.4 vs. 71.2; p < 0.001). Knowledge correlated positively with usage intensity (r = 0.536; p < 0.001), whereas legal concern correlated negatively (r = −0.426; p < 0.001). In multivariable models, younger age (OR = 0.91; p = 0.004), knowledge (OR = 1.06; p = 0.005), and trust (OR = 1.07; p = 0.005) independently predicted adoption. The linear model explained 46.1% of usage variance. Stratified analysis suggested legal concern attenuated the knowledge–usage slope (β: 0.51→0.18); however, the formal knowledge-by-concern interaction term was not statistically significant (p = 0.191), and this pattern is therefore exploratory. Conclusions: In this modest, single-centre sample, AI adoption was independently associated with knowledge and trust, and legal concern was independently and negatively associated with usage intensity; the apparent dampening of the knowledge–usage relationship by legal concern was suggestive but not statistically confirmed. Targeted legal-regulatory literacy and structured training may support practical AI uptake in public health settings, pending confirmation in larger, multicentre studies.
Carla Aurelia Stoiacovici, A. Ilie, Felicia Marc et al.· Healthcare· 0 citations
Background: Artificial intelligence (AI)-based telemedicine increasingly supports remote diagnosis, clinical decision support, and technology-mediated health communication. However, patient trust remains theoretically fragmented because prior studies often treat it as a general adoption variable rather than as a social psychological process. This review aimed to synthesize how patient trust in AI-based telemedicine is formed through social cognition, human-AI interaction, and institutional legitimacy. Method: A systematic literature review was conducted following PRISMA 2020. Scopus was the primary database, while PubMed and Google Scholar were used as complementary sources. Searches covered peer-reviewed literature published from 2015 to 2025 using Boolean combinations of trust, social acceptance, social psychology, human-AI interaction, artificial intelligence, telemedicine, telehealth, and virtual care. Two reviewers independently screened records, extracted data using a predefined matrix, and appraised empirical studies using the Mixed Methods Appraisal Tool, while conceptual papers were assessed for relevance, theoretical clarity, and contribution. Heterogeneity was handled through narrative thematic synthesis. Results: Eleven studies/reports were included. Three trust dimensions emerged: individual cognitive appraisal, including explainability, perceived risk, and prior experience; interactional attribution, including AI agency, role clarity, and communication quality; and socio-structural legitimation, including privacy, fairness, bias mitigation, and institutional governance. Human-AI interaction functioned as the experiential channel through which trust was calibrated, while social acceptance operated as a collective condition shaped by norms, professional identity, and legitimacy. Conclusion: Patient trust in AI-based telemedicine is a multilevel social psychological process. Sustainable implementation requires transparent design, privacy assurance, bias governance, and clear alignment between AI systems and clinical roles.
Alexander Sitepu, Maharani Laillyza Apriasari, Alvi Yasmina et al.· Lentera Negeri· 0 citations