Relevance of advanced imaging analysis units in radiology departments: a narrative review.
Authors
Affiliations (4)
Affiliations (4)
- MRI Section, Radiology Department, HT Medica, Carmelo Torres 2, 23007, Jaén, Spain. [email protected].
- Engineering Department, HT Medica, Carmelo Torres 2, 23007, Jaén, Spain.
- Department of Neuroradiology, McGovern Medical School, The University of Texas Health Science Center at Houston, 6431 Fannin St., Houston, TX, 77030, USA.
- MRI Section, Radiology Department, HT Medica, Carmelo Torres 2, 23007, Jaén, Spain.
Abstract
Radiology departments (RDs) face an increasing volume of data, images, and information, leading to a higher workload for radiologists. The integration of artificial intelligence (AI) presents an opportunity to optimize workflows and reduce the burden on radiologists. This review explores the role of advanced imaging analysis units (AIAUs) in enhancing radiological processes and improving overall patient outcomes. A literature review was conducted to assess the impact of AI-driven AIAUs on RD workflows. The study examines the collaboration between radiologists, technicians, and biomedical engineers in the extraction and processing of imaging data. Additionally, the integration of AI algorithms for task automation is analyzed. The implementation of AIAUs in RDs has the potential to enhance workflow efficiency by minimizing radiologists' workload and improving imaging analysis. These units facilitate collaborative work among radiologists, technicians, and engineers, fostering continuous communication, feedback, and training. AI algorithms incorporated into AIAUs support automation, streamlining pre- and postprocessing imaging tasks. AIAUs represent a promising approach to optimizing RD workflows and improving patient outcomes. Their successful implementation requires a multidisciplinary approach, integrating AI technologies with the expertise of radiologists, technicians, and biomedical engineers. Continuous collaboration and education within these units will be essential to maximize the benefits of emerging digital technologies in radiology.