From Darkrooms to Deep Learning: A Cross-Sectional Analysis of the Evolution, Workflow, and Diagnostic Transformation of Radiologic Practice

Abstract

Radiology has transitioned from a manual, chemical-reliant specialty into a sophisticated digital domain integrated with Artificial Intelligence (AI) and Deep Learning. While technical advancements in diagnostic accuracy are well-documented, there is a critical research gap regarding how radiologic professionals adapt to these shifting workflows, particularly in developing healthcare contexts like the Philippines. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), this study analyzed the perception of practice evolution, workflow attitudes, and digital transformational adaptability among radiologic technologists. This study utilized a cross-sectional descriptive-correlational design. Data were obtained from 71 licensed radiologic technologists through a validated 30-item instrument exhibiting high internal consistency (Overall alpha = .892). Statistical analyses specifically weighted means, standard deviations, and Pearson product-moment correlation were performed after verifying normality and linearity assumptions. The findings indicated a "Very High" level of perception concerning practice evolution (M = 3.40), workflow attitudes (M = 3.39), and digital adaptability (M = 3.36). Furthermore, correlation analysis revealed strong and statistically significant relationships between technologist perception and workflow efficiency (r = .782, p < .001), as well as between perception and digital adaptability (r = .815, p < .001). The study concludes that while technologists demonstrate high resilience and openness to AI integration, challenges remain in professional role redefinition and technical troubleshooting autonomy. These results provide empirical evidence for the need to strengthen radiologic informatics curricula and institutional policies that ensure AI-driven efficiency gains support patient-centered care.