1 Patient Perspectives on the Implementation of Artificial Intelligence in Radiology: Development, Validation, and Standardization of a Questionnaire https://brieflands.com/journals/ijradiology/articles/167513 10.5812/iranjradiol-167513 1 Background :Patient trust and acceptance are critical for the successful implementation of artificial intelligence (AI) in clinical radiology. Current patient perceptions are often influenced by concerns about data privacy, accountability, and the potential dehumanization of medical care. Objectives :This study aimed to culturally adapt and validate a standardized questionnaire for assessing patient perspectives on AI in radiology and to identify the underlying latent dimensions characterizing these perspectives. Methods :This study culturally adapted and validated the questionnaire originally developed by Ongena et al., which includes 39 Likert-scale items measuring five factors: distrust and accountability, procedural knowledge, personal interaction, efficiency, and being informed. The study included 347 patients from diagnostic imaging departments in Tehran, Iran. Statistical analyses were performed using IBM SPSS Statistics for Windows, version 21.0, and IBM SPSS Amos, version 18.0 (IBM Corp., Armonk, NY, USA). Confirmatory factor analysis (CFA) was used to validate the questionnaire structure and to identify key influencing factors. Results :The adapted questionnaire demonstrated excellent reliability (Cronbach alpha = 0.92) and good model fit (comparative fit index [CFI] = 0.911; root mean squared error of approximation [RMSEA] = 0.066). Confirmatory factor analysis showed that personal interaction (path coefficient = 0.98) and being informed (path coefficient = 0.81) were most strongly associated with the overall construct of patient perspectives, whereas efficiency (path coefficient = 0.07) had a minimal association. Conclusions :These findings suggest that, for AI to be successfully integrated into radiology, implementation strategies should prioritize human-centered elements. Healthcare professionals should emphasize clear communication and educate patients that AI is a complementary tool to human expertise, rather than a replacement, to foster trust and encourage adoption. 1 11 Mahdiye Rahimi Department of Health Services Management, North Tehran Branch, Islamic Azad University, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran Iran mahdiyerahimi84@gmail.com Mohammadkarim Bahadori Health Management Research Center, Baqiatallah University of Medical Sciences, Tehran, Iran Iran bahadorihealth@gmail.com Khalil Alimohammadzadeh Department of Health Services Management, NT. C., Islamic Azad University, Health Economics Policy Research Center, TeMS. 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1 Features of Artificial Intelligence Driven Pioneer Hospitals: Insights from Radiology https://brieflands.com/journals/ijradiology/articles/166577 10.5812/iranjradiol-166577 1 Context :Artificial intelligence (AI) has a critical role in hospitals that have successfully implemented it and achieved international rankings, because these institutions can serve as benchmarks for other hospitals. The main objective of this article was to examine the characteristics of pioneer hospitals in AI, with a focus on radiology departments. Evidence Acquisition :This narrative review was performed according to SANRA guidelines. Data were collected by searching PubMed, Google Scholar, and ScienceDirect for articles published between January 2019 and June 2025, using search strings that included ('artificial intelligence' OR 'AI') AND ('pioneer hospital') AND ('adoption' OR 'implementation') AND ('radiology'). After screening and selecting 41 papers for the final study, the characteristics of pioneer hospitals were determined. Two independent reviewers then confirmed the common features of pioneer hospitals through discussion and consensus. Risk of bias and reliability were assessed based on study design and transparency of reporting, and grey literature was used cautiously and clearly marked with access dates. Results :Hospital type, innovation, transparency, strong leadership, learning, and hospital size were identified as characteristics of pioneer hospitals. These hospitals have used AI in radiology departments to analyze data, predict outcomes, support clinical decision-making and operational efficiency, and improve diagnostic capabilities and workflow. Conclusions :Successful AI integration in pioneer radiology departments depends on synergy between technical readiness and organizational factors. Hospitals can enhance diagnostic accuracy and operational efficiency by prioritizing strong leadership, a collaborative culture, and a clear digital strategy alongside technological investment. 1 9 Maryam Goodarzi Tehran University of Medical Sciences, Tehran, Iran Iran m-goodarzi@farabi.tums.ac.ir Mashallah Torabi Research Center for Science and Technology in Medicine, Service Desk and Office Automation, Tehran University of Medical Sciences, Tehran, Iran Iran mtorabi@tums.ac.ir Reza Safdari Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran Iran rsafdari@tums.ac.ir Maryam Ahmadi Service Desk and Office Automation, Tehran University of Medical Sciences, Tehran, Iran Iran m-ahmadi@farabi.tums.ac.ir Samira Elmi Service Desk and Office Automation, Tehran University of Medical Sciences, Tehran, Iran Iran s-elmi@farabi.tums.ac.ir Fatemeh Golmahi Service Desk and Office Automation, Tehran University of Medical Sciences, Tehran, Iran Iran fgolmahi@farabi.tums.ac.ir Samira Mortezaie Service Desk and Office Automation, Tehran University of Medical Sciences, Tehran, Iran Iran s-mortezaei@farabi.tums.ac.ir Parisa Nezari Service Desk and Office Automation, Tehran University of Medical Sciences, Tehran, Iran Iran nezari1362@gmail.com Mohammad Ali Souraki Faculty of Medical, Tehran University of Medical Sciences, Tehran, Iran Iran ma.souraki51@gmail.com Saeedeh Shantia Electronic Organization and Service Desk, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran Iran workshantia57@gmail.com Nafiseh Ghavami Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran Iran ghavamina@gmail.com No Keyword 2.pdf [0]Jovy-Klein F, Stead S, Salge TO, Sander J, Diehl A, Antons D.Forecasting the future of smart hospitals: findings from a real-time delphi study. 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