Paper Title
A SURVEY ON MEDICAL IMAGING ANNOTATION TOOLS

Abstract
Artificial Intelligence has dominated the leading position in the constantly growing advancements in technology and is redesigning the approach to diagnosis and patient care by healthcare professionals. It has also opened up the scope for compiling the recent developments in the field of Medicine and healthcare. The applications are powered by potent algorithms which serve as the backbone of innovation. In the fast-paced world, it sometimes becomes difficult for even efficient medical experts to diagnose abnormalities effectively. There comes to rescue the labeled or annotated highly reliable image datasets and the models built through potent algorithms, which can help surgeons and healthcare professionals make not only optimal but most promising diagnoses. The datasets that are enriched by annotation help machine learning models find patterns, discover anomalies, and give actionable insights. Medical imaging is among the areas that rely heavily on annotation in building AI models. It deals with labeling images in terms of specific details such as the shape, location, and characteristic features of the abnormalities. It is an essential process in sensitive fields like oncology, cardiology, and radiology where even minor diagnostic errors have significant consequences. Several tools, designed to accommodate different imaging modalities, like X-rays, MRIs, CT scans, and ultrasounds support medical image annotation. But testing functionalities of all available tools is not possible by a professional and thus in this literature, we are presenting a structured report of different annotation tools in use for the annotation of medical images. Keywords - Medical Image Annotation, Deep Learning, AI In Healthcare, Data Labeling.