Patient Perspectives on the Implementation of Artificial Intelligence in Radiology: Development, Validation, and Standardization of a Questionnaire
| Author | Mahdiye Rahimi | en |
| Author | Mohammadkarim Bahadori | en |
| Author | Khalil Alimohammadzadeh | en |
| Author | Seyed Mojtaba Hosseini | en |
| Orcid | Mahdiye Rahimi [0000-0003-4726-6052] | en |
| Orcid | Mohammadkarim Bahadori [0000-0002-7157-9908] | en |
| Orcid | Seyed Mojtaba Hosseini [0000-0002-0029-4622] | en |
| Issued Date | 2026-06-30 | en |
| Abstract | 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. | en |
| DOI | https://doi.org/10.5812/iranjradiol-167513 | en |
| URI | https://brieflands.com/journals/ijradiology/articles/167513 | en |
| Keyword | Artificial Intelligence | en |
| Keyword | Radiology | en |
| Keyword | Patient Attitudes | en |
| Keyword | Surveys And Questionnaires | en |
| Keyword | Trust | en |
| Publisher | Brieflands | en |
| Title | Patient Perspectives on the Implementation of Artificial Intelligence in Radiology: Development, Validation, and Standardization of a Questionnaire | en |
| Type | Research Article | en |
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