Health Informatics in Radiology: Enhancing Imaging Analysis and Workflow Efficiency

Abstract

Radiology has many AI tool applications that could make great use of the resources that are accessible. It has technical roots and is naturally full of data that can be retrieved, analyzed, and used to make departmental processes better. To improve the effectiveness of AI model implementation, it is also important to combine clinical knowledge about the situation with technological advances. The performance of AI models that use a patient's clinical data in the setting of their specific condition is better. As database technology gets better, it will be easier to collect, track, and evaluate report data. It will also be easier to make decision-making tools at the point of care that are based on data and are automatic. This method helps radiologists by giving them data-driven analyses that lead to better diagnostic and clinical outcomes. It also gives them objective and complete views into ambiguity, helping them find its root causes and giving them data-driven evaluations.