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.
Abstract
Background: Artificial Intelligence (AI) is rapidly transforming healthcare. However, its impact on the core of medical practice, the doctor-patient relationship, remains a subject of debate, particularly regarding patients' trust.
Methods: A cross-sectional study was conducted to assess patients’ perceptions of the integration of Artificial Intelligence (AI) in healthcare and its impact on the doctor–patient relationship and participants’ trust, comfort, and concerns toward AI use. The study was carried out on 258 participants recruited from outpatient clinics at Mutah University Health Center and Al-Karak Hospital from December 2025 to March 2026. Data were collected through a structured questionnaire by face-to-face interview. Statistical analysis was performed using SPSS version 27 and relationships were analyzed using non-parametric tests including the Mann-Whitney U test and the Kruskal-Wallis test.
Results: An overall of 258 participants were enrolled in the study, with the majority of them aged between 18 and 25 years, and a higher proportion of females (73.3%). 76.7% of the participants had university degrees. Students’ knowledge of AI varied significantly by age (p < 0.001) and occupation (p < 0.001) with younger students having higher proficiency. Education level was a significant predictor of attitudes toward the role of AI in enhancing the doctor-patient relationship (p = 0.038).
Conclusion: Participants generally express optimism about AI's efficiency in healthcare, especially regarding time savings. However, comfort and trust in AI are significantly affected by factors such as gender and digital literacy.
Positive correlations were identified among all measured dimensions, with the strongest association observed between hope and adaptation, and the findings indicate that students’ evaluations of artificial intelligence involve interrelated perceptions of knowledge, anxiety, positive expectations, and educational preparation needs.
Background: Artificial intelligence (AI) is increasingly being applied in medicine for diagnosis, treatment, and decision-making. While enthusiasm for AI training among healthcare workers has been reported globally, little is known about awareness and attitudes in Afghanistan, where limited access to advanced diagnostic tools makes AI particularly valuable. Objective: This study aimed to assess the knowledge, attitude, and practices of AI among healthcare workers in Kabul City. Methods: A cross-sectional survey was conducted from January to February 2025 among 256 healthcare staff, including physicians, nurses, technicians, and administrative personnel. Data were collected using a structured questionnaire distributed online and in paper format. Responses were recorded on a three-point Likert scale. Statistical analysis was performed using SPSS version 26, employing descriptive statistics, chi-square tests, regression, and correlation analyses. Reliability was assessed using Cronbach’s alpha. Results: Of the participants, 71.1% were male (28.9% were female), and 43.8% were aged 21–29 years. Knowledge of AI was limited: only 1.6% demonstrated good knowledge, 44.7% poor knowledge, and 53.9% had insufficient knowledge. Attitudes were more favorable, with 47.3% expressing positive views, 37.5% somewhat agreeing, and 15.2% expressing negative views. Regression analysis revealed that age was significantly associated with knowledge scores, and knowledge strongly predicted positive attitudes. Reliability analysis confirmed acceptable internal consistency across domains (α ≥ 0.72). Conclusion: Knowledge of AI among medical staff in Kabul is limited, but attitudes are generally favorable. Structured training programs, conferences, and AI-enabled systems are needed to strengthen Afghanistan’s healthcare sector.
Ahmad Mustafa Rahimi, Abdul Bashir Bashari, M. Mohammadi et al.· Annals of Medicine and Surge...· 0 citations
The findings suggest that students' attitudes toward AI are shaped not only by technological interest but also by perceptual factors related to AI, and integrating clinically oriented AI content and awareness-building activities into medical education may support the development of more balanced and informed attitudes toward AI.
Batuhan Horasan, A. Ergin, Eda Şenarabacı· BMC Medical Education· 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 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
The normalisation of АІ in academic practice indicates its role as a cognitive extension in medical education, which necessitates the development of structured educational strategies and methodological guidelines for its responsible use in medical training.
Inna I. Kucherenko, A. O. Burdeinyi, L. Lymar et al.· Polski merkuriusz lekarski :...· 0 citations
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