Dear Colleagues,

In 2020, there were 2.3 million women diagnosed with breast cancer and 685 000 deaths worldwide. As of the end of 2020, 7.8 million women had been diagnosed with breast cancer in the previous five years, making it the world's most prevalent cancer. Women lose more disability-adjusted life years (DALYs) to breast cancer than to any other type of cancer worldwide.  Breast cancer occurs in every country around the world in women at any age after puberty, with rising incidence in later life.  From the 1930s until the 1970s, breast cancer mortality varied little.  Survival rates began to rise in the 1980s in countries with early detection programs combined with various kinds of therapy to eradicate invasive illnesses (https://www.who.int/news-room/fact-sheets/detail/breast-cancer). The World Health Organization (WHO) announced a new Global Breast Cancer Initiative Framework Monday, outlining a plan to save 2.5 million lives from breast cancer by 2040. It includes three action pillars:  1. Breast cancer early-detection programs, with the goal of detecting and treating at least 60% of breast cancers in their early stages. 2. Breast cancer outcomes can be improved if diagnosed within 60 days of the initial appearance. 3. Breast cancer management so that at least 80% of patients finish their recommended treatment. (https://www.who.int/news/item/03-02-2023-who-launches-new-roadmap-on-breast-cancer). Finding breast cancer early and receiving cutting-edge cancer treatment are two of the most critical techniques for reducing breast cancer fatalities. Early detection refers to discovering and diagnosing an illness before symptoms appear. Mammograms, breast ultrasounds, breast MRIs, and newer and experimental breast imaging procedures can all be used to detect and diagnose breast cancer. The advancement of image-based artificial intelligence (AI)-assisted tumor diagnosis is an effective technique for early detection and boosting imaging diagnosis efficiency and accuracy. AI can recognize, segment, and diagnose tumor lesions automatically by learning from imaging data and constructing algorithm models.

Thus, we are interested in the collective present successes in AI-assisted imaging diagnosis and will address clinical advances in AI-based imaging detection of breast cancer accurate diagnosis.

For this, it is crucial to collect the findings of many studies that have demonstrated the efficacy of artificial intelligence (AI)-based imaging detection applications in breast cancer diagnosis for this purpose.

So, research articles, reviews and studies in this area of study are welcome. We look forward to receiving your contributions.

Dr. Eka Miranda

Section editor