Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a “one-stop center” synthesis and provide a holistic bird’s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

Histopathology techniques offer a unique way to study the structural and functional characteristics of biological model systems such as cultured cells, tissues and organoids. As the field of histopathology advances and more complex properties of living organisms are revealed through novel assays, there is a growing need for image analysis methods that are robust and easy to use. In many histopathology image analysis workflows, the first step is to segment cell nuclei, as they serve as the fundamental block for identifying individual cells in histopathology images. Such methods are crucial in a range of research studies, from counting cells and tracking moving populations to localizing proteins, classifying phenotypes and profiling treatments. Here, this review intended to provide an updated cell annotation system for histopathology images using the predetermined search strings and a set of inclusion criteria. Accordingly, 11 cell annotation systems (i.e., Image J, ASAP, Ilastik, Quanti.us, JS Segment Annotator, Labelme annotation tool, Labelbox, LabelImgTool, OpenSurfaces Segmentation UI, Cell Profiler and MATLAB Image Segmenter) was included. An in-depth discussion on the background of each included system was provided in this review alongside a brief comparison across different cell annotation systems.