International journal of medical informatics · 2026
Acceptance and trust of general population toward medical artificial intelligence across three MENA countries: A multicountry cross-sectional study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hussein Mohamed Fakhry, Othman Sally Samir, Ismail Ghada Mahmoud, Abdelmoneim Shaimaa Abdelaziz, Naguib Manal, El-Shistawy Mai, Gebreal Assem, Alabden Hassan Mai Zain, Alshehri Mohammed Mubarak, Alabdo Nayef, Abouelmagd Moaz Elsayed, Ghazy Ramy Mohamed
Аннотация
BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare, improving diagnostics, treatment, and healthcare management. However, public acceptance and trust are essential for its successful adoption.
OBJECTIVE: To assess and compare acceptance and trust toward using AI in healthcare among general population in three selected Arab countries in the Middle East and North Africa (MENA) region and identify their associated factors.
METHODS: A multi-country cross-sectional study was conducted between October 2025 and February 2026 among 1250 adults from Kingdom of Saudi Arabia (KSA), Egypt, and Syria. Data were collected using online and face-to-face questionnaires assessing sociodemographic characteristics, acceptance of AI use in healthcare, and trust in its medical application. Acceptance and trust scores were rescaled to a 0-100 range. Unadjusted comparisons were performed using Mann-Whitney U and Kruskal-Wallis tests, while multiple linear regression models were used to identify factors independently associated with acceptance and trust.
RESULTS: Participants had a median age of 23.0 years (interquartile range (IQR): 8.0)), 61.0% were women. Most participants were aware of AI applications in medicine (96.2%), and 87.7% had previously used AI in a medical context. Egypt and KSA had significantly higher acceptance (β = 14.6, 95% CI: 11.8-17.3 and β = 2.8, 95% CI: 0.3-5.2, respectively) and trust scores (β = 18.7, 95% CI: 15.7-21.7 and β = 3.1, 95% CI: 0.4-5.7 repectively) than Syria (p < 0.05). Prior use of AI in healthcare was independently associated with both greater acceptance (β = 9.7, 95% CI: 6.9-12.6), and trust (β = 3.4, 95% CI: 0.3-6.5), while AI awareness was independently associated with acceptance (β = 7.5, 95% CI: 2.7-12.3). Acceptance was higher among males (β = 2.5, 95% CI: 0.6-4.4), and those reporting sufficient income with or without savings (β = 2.4, 95% CI: 0.04-4.7 and 3.9,95% CI: 1.1-6.8 respectively). Trust was higher among married participants (β = 3.2, 95% CI: 0.2-6.3), housewives (β = 4.7, 95% CI: 1.2-8.3), skilled craftsmen (β = 6.4, 95% CI: 1.6-11.2).
CONCLUSION: Public acceptance and trust toward AI use in healthcare were generally favorable but varied substantially across the three studied Arab MENA countries. Enhancing AI literacy may improve public acceptance and trust of medical AI.
OBJECTIVE: To assess and compare acceptance and trust toward using AI in healthcare among general population in three selected Arab countries in the Middle East and North Africa (MENA) region and identify their associated factors.
METHODS: A multi-country cross-sectional study was conducted between October 2025 and February 2026 among 1250 adults from Kingdom of Saudi Arabia (KSA), Egypt, and Syria. Data were collected using online and face-to-face questionnaires assessing sociodemographic characteristics, acceptance of AI use in healthcare, and trust in its medical application. Acceptance and trust scores were rescaled to a 0-100 range. Unadjusted comparisons were performed using Mann-Whitney U and Kruskal-Wallis tests, while multiple linear regression models were used to identify factors independently associated with acceptance and trust.
RESULTS: Participants had a median age of 23.0 years (interquartile range (IQR): 8.0)), 61.0% were women. Most participants were aware of AI applications in medicine (96.2%), and 87.7% had previously used AI in a medical context. Egypt and KSA had significantly higher acceptance (β = 14.6, 95% CI: 11.8-17.3 and β = 2.8, 95% CI: 0.3-5.2, respectively) and trust scores (β = 18.7, 95% CI: 15.7-21.7 and β = 3.1, 95% CI: 0.4-5.7 repectively) than Syria (p < 0.05). Prior use of AI in healthcare was independently associated with both greater acceptance (β = 9.7, 95% CI: 6.9-12.6), and trust (β = 3.4, 95% CI: 0.3-6.5), while AI awareness was independently associated with acceptance (β = 7.5, 95% CI: 2.7-12.3). Acceptance was higher among males (β = 2.5, 95% CI: 0.6-4.4), and those reporting sufficient income with or without savings (β = 2.4, 95% CI: 0.04-4.7 and 3.9,95% CI: 1.1-6.8 respectively). Trust was higher among married participants (β = 3.2, 95% CI: 0.2-6.3), housewives (β = 4.7, 95% CI: 1.2-8.3), skilled craftsmen (β = 6.4, 95% CI: 1.6-11.2).
CONCLUSION: Public acceptance and trust toward AI use in healthcare were generally favorable but varied substantially across the three studied Arab MENA countries. Enhancing AI literacy may improve public acceptance and trust of medical AI.