The study found that healthcare practitioners in Riyadh generally perceived AI as a potentially valuable tool for supporting patient safety culture, particularly through improving diagnostic accuracy and reducing errors; however, concerns about data privacy and insufficient training remain significant barriers that must be addressed to ensure effective and safe AI integration.
Abstract
Background
This cross-sectional study investigates healthcare practitioners' perceptions of the implementation of artificial intelligence (AI) to enhance patient safety culture in Riyadh, Saudi Arabia, conducted from March to June 2025. The focus is on understanding how AI is perceived in the context of improving patient safety culture and aligning with Saudi Vision 2030 goals.
Methods
The study employed a cross-sectional design and administered self-administered online surveys via convenience sampling among healthcare practitioners across multiple healthcare settings in Riyadh. The study targeted a population including doctors, nurses, and allied health professionals. A structured questionnaire was developed to assess perceptions of AI, including key variables such as perceived AI benefits, concerns about data privacy, and the necessity of training. Descriptive statistics were computed to characterize the sample, including age, gender, and profession.
Results
Findings revealed that most participants perceived AI as supportive of patient safety through improved diagnostic accuracy, reduced medical errors, and streamlined workflows, which participants believed may contribute to a stronger patient safety culture. Descriptive analyses suggested variation in perceptions across professional groups.
Discussion
The study found that healthcare practitioners in Riyadh generally perceived AI as a potentially valuable tool for supporting patient safety culture, particularly through improving diagnostic accuracy and reducing errors; however, concerns about data privacy and insufficient training remain significant barriers that must be addressed to ensure effective and safe AI integration.
Conclusions
This study highlighted healthcare practitioners' positive perception of AI's role in enhancing patient safety culture in Riyadh. While AI is seen as beneficial in improving accuracy and reducing errors, challenges such as data privacy concerns and a lack of training were identified as barriers to its implementation.
CLINICAL TRIAL NUMBER
Not applicable.
Artificial Intelligence (AI) is rapidly transforming healthcare delivery worldwide. The adoption of AI among healthcare professionals in Nigeria, its patterns of use, and associated factors are emerging issues that need to be explored. This study aimed to assess the prevalence, patterns, and determinants of AI use among healthcare professionals in Lagos State, Nigeria. A descriptive cross-sectional study was conducted among 415 healthcare professionals across 14 public health facilities in Lagos, representing primary, secondary, and tertiary levels of care. Data were collected using a structured, self-administered questionnaire and analysed using descriptive statistics, chi-square tests, and binary logistic regression. Although only 19.8% had received formal AI training, 86.5% reported using AI and 51.3% used it specifically for professional duties. The most common professional application was diagnostic support, including clinical reasoning, interpretation of laboratory and radiological findings, and medication-interaction checks. AI use in professional duties was significantly associated with educational level (p = 0.042); postgraduate-qualified professionals were twice as likely to use AI as those with only basic healthcare degrees (OR = 2.07, p = 0.014). Key barriers included privacy concerns, algorithmic distrust, and limited formal training. Despite low formal training, use of AI-related digital tools was common among healthcare professionals in public health facilities in Lagos, primarily supporting diagnostic reasoning and clinical documentation. These findings highlight the need for structured AI literacy programs and institutional governance frameworks to support safe and equitable AI integration in Nigeria’s health system.
A. Chima-Oduko, B. Maduafokwa, K. Abdulraheem et al.· BMC Digital Health· 0 citations
Background 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. Objectives 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. Methods 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. Results 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. Conclusion 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. Practical Implications 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
Undergraduate medical students show moderate awareness of AI in healthcare but lack formal training and in-depth understanding, highlighting the need for structured AI education within medical curricula and further research on its long-term impact.
Sonali Sharma, Smriti Kayat, N. Saboo et al.· Nigerian Medical Journal· 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
Assessment of patients’ perceptions of the integration of Artificial Intelligence in healthcare and its impact on the doctor–patient relationship and participants’ trust, comfort, and concerns toward AI use found comfort and trust in AI are significantly affected by factors such as gender and digital literacy.
Faten M. Rabie, Youmna Mamdouh, Rama Ah. Alabadd et al.· PAIN, JOINTS, SPINE· 0 citations
BACKGROUND
Successful implementation of artificial intelligence (AI) in healthcare depends not only on technological performance but also on the readiness of healthcare professionals to understand, trust, and appropriately use AI-enabled systems. Evidence regarding AI knowledge among multidisciplinary operating room (OR) staff remains limited, particularly in low- and middle-income healthcare settings.
OBJECTIVE
To assess AI-related knowledge and perceptions among OR personnel and examine demographic factors associated with workforce preparedness for AI implementation in clinical practice.
METHODS
An analytical cross-sectional survey was conducted among 200 OR healthcare professionals, including nurses, OR technicians, and anesthesiology personnel, working in four educational hospitals affiliated with Isfahan University of Medical Sciences, Iran. Data were collected using a researcher-developed questionnaire, validated for content and construct validity, consisting of demographic characteristics, seven knowledge items, and twenty-five perception items. Because the data were not normally distributed, Mann-Whitney U, Kruskal-Wallis, and Spearman correlation tests were used for statistical analysis.
RESULTS
Participants demonstrated limited AI knowledge (mean score: 2.11 ± 0.76 out of 7; 30.1% of the maximum possible score), whereas perceptions toward AI were generally positive (mean score: 76.8 ± 14.5 out of 125). Knowledge scores differed significantly according to educational level (P < 0.001), employment status (P < 0.001), professional discipline (P = 0.026), work experience (P = 0.015), and gender (P = 0.036). Perception scores were significantly associated only with educational level (P = 0.010). Knowledge and perception showed a moderate positive correlation (Spearman's r = 0.421, P < 0.001), while age was weakly negatively correlated with knowledge (r = - 0.156, P = 0.027).
CONCLUSIONS
Despite favorable attitudes toward AI, OR staff demonstrated substantial deficiencies in AI knowledge. Further research and targeted educational initiatives may be warranted to improve AI knowledge among perioperative healthcare professionals and support the safe integration of clinical informatics.
Farzaneh Nekuei manesh, Zahra Eydizadeh, M. Sohrabi et al.· International Journal of Med...· 0 citations
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