Acceptance of AI-Driven Radiography among Radiographers in Iligan City: A Factor Analytic Study
Georgie Jane Lorena, Bazim Daluma, Linah Badroden, Mark Alipio
Received: 18 September 2025; Revised: 21 October 2025; Accepted: 12 November 2025; Published: 16 November 2025
DOI: https://doi.org/10.66074/PL09OK87
Abstract
Artificial intelligence (AI) now shapes image acquisition, reconstruction, and interpretation in radiology, yet little is known about how frontline radiographers in lower middle income settings receive these tools. This study examined the latent dimensions of acceptance of AI driven radiography among hospital based radiographers in Iligan City, Philippines. A cross sectional survey of 120 licensed radiographers used a 25 item Likert scale based on the technology acceptance model and current AI in radiography literature. Principal axis factoring with varimax rotation extracted four factors: AI performance and impact, AI adoption and adaptability, fear of AI displacement, and collaborative AI for patient care. The model showed strong sampling adequacy (Kaiser Meyer Olkin = .81) and significant Bartlett’s test of sphericity, χ²(300) = 1077.81, p < .001. The four factor solution explained 66.2 percent of total variance, with Cronbach’s alpha values between .82 and .90. Radiographers reported high acceptance of AI when it improved image quality, workflow efficiency, and patient outcomes, while moderate concern about job displacement persisted. The results highlight the need for structured AI education and governance that protect roles while leveraging AI for safer, more efficient radiography services.
Keywords: AI acceptance, image quality, radiographers, workflow efficiency
Author Information: Iligan Medical Center College, Iligan, Philippines; [email protected]
Volume 2, Issue 1, March 2026