From Image Acquisition to Algorithmic Accountability: Reframing Research Methodology in Radiologic Technology for the Age of Artificial Intelligence
Mark Alipio
Received: 02 February 2026; Revised: 29 May 2026; Accepted: 06 June 2026; Published: 08 June 2026
DOI: https://doi.org/10.66074/R3B7H4D9
Abstract
Artificial intelligence has progressed from experimental image classification to tools influencing acquisition, triage, reconstruction, image quality assessment, dose optimization, workflow, education, and departmental governance. However, medical imaging literature often remains model-centered, emphasizing area under the curve, sensitivity, specificity, or reader performance, while many Radiologic Technology studies focus on perceptions, readiness, and attitudes. This narrative review reframes AI-related Radiologic Technology research as a methodological issue rather than a technology-adoption topic. It argues that radiologic technologists shape how imaging data are produced, interpreted, repeated, rejected, archived, and used for algorithmic learning. Thus, AI-era research must treat acquisition protocols, positioning, exposure parameters, dose indices, patient preparation, equipment variation, image quality, workflow behavior, trust, override decisions, and post-deployment monitoring as core design elements. Synthesizing recent AI reporting standards and evaluation frameworks, the review identifies gaps in validation, human-AI interaction, implementation, generalizability, equity, accountability, and AI literacy. It proposes the RADIATE-AI framework to guide safer, locally valid, and methodologically mature studies.
Keywords: artificial intelligence, education, radiography, radiologic technology, research
Author Information: Iligan Medical Center College, Philippines; mark.alipio@imcc.edu.ph
Volume 2, Issue 2, June 2026