A Radiologist's Perspective of Medical Annotations for AI Programs: The Entire Journey from Its Planning to Execution, Challenges Faced

Abstract

Artificial intelligence (AI) in radiology and medical science is finding increasing applications with annotations being an integral part of AI development. While annotation may be perceived as passive work of labeling a certain anatomy, the radiologist plays a more important role in this task apart from marking the structures needed. Apart from annotation, more important aspect of their role is planning the anatomies/pathologies needed, type of annotations to be done, choice of the annotation tool, training the annotators, planning the duration of annotation, etc. A close interaction with the technical team is a key factor in the success of the annotations. The quality check of both the internally and externally annotated data, creating a team of good annotators, training them, and periodically reviewing the quality of data become an integral part of their work. Documentation related to the annotation work is another important area where the clinician plays an integral role to comply with the Food and Drug Administration requirements, focused on a clinically explainable and validated AI algorithms. Thus, the clinician becomes an integral part in the ideation, design, implementation/execution of annotations, and its quality control. This article summarizes the experiences gained during planning and executing the annotations for multiple annotation projects involving various imaging modalities with different pathologies.

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