Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

Abstract

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.

Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal–Ethical Considerations

“… 43 Additionally, progress in radiogenomics, transcriptomics, and metabolomics enables AI to process intricate multidimensional data, offering unique advantages in assessing breast cancer heterogeneity and broadening the possibilities for a comprehensive exploration of breast cancer’s pathophysiological mechanism in precision medicine. 44 - 49 …”

Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics

“…It involves a quantitative analysis of bibliometric metadata in scientific research (7). Previous scientometrics studies have analyzed the growth of AI in different fields of medicine (8)(9)(10)(11) and in biomedicine as general (12). Recent scientometrics studies reviewed the impact of AI on liver cancer especially about the medical image diagnosis of cholangiocarcinoma (13,14).…”

Breast cancer screening outcomes and risk assessment among women of Abha city, Saudi Arabia

“…Magnetic resonance imaging (MRI) also complements MMG by increasing the tendency to detect breast lesions but it is done by using contrast and it is quite expensive as well so the combination of MMG and MRI are not recommended together for screening, although sensitivity is about 88.1% for MRI [27]. Breast self-examination (BSE) can also be done but because of its low sensitivity of 12-14% and high index of false positive results, it is not recommended as a screening tool now [28,29]. This study focuses on the screening of breast cancer by MMG and USG for early detection of breast cancer and identification of risk factors for the development of breast cancer.…”