International Journal of Innovative Research in Computer Science and Technology (IJIRCST) ISSN(Online): 2347-5552, Volume-12, Issue-2, March 2024 https://doi.org/10.55524/ijircst.2024.12.2.22 Article ID IJIRD-1284, Pages 126-129 www.ijircst.org Innovative Research Publication 126 A Review of AI in Breast Cancer Detection Abhilasha 1 , Ashima Narang 2 , and Priyanka Vashisht 3 1 MCA Scholar, Department of Computer Application, Amity University, Gurugram, Haryana, India 2 Assistant Professor, Department of Computer Science & Engineering, Amity University, Gurugram, Haryana, India 3 Associate Professor, Department of Computer Science & Engineering, Amity University, Gurugram, Haryana, India Correspondence should be addressed to Abhilasha; Received 8 March 2024; Revised 20 March 2024; Accepted 28 March 2024 Copyright © 2024 Made Abhilasha et al. This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT- Cancer stands out as one of the most pressing global health challenges, and over the past decade, significant advancements have been made in diagnostic tests and methodologies. These tests fall into categories such as imaging tests, and endoscopic procedures, generating substantial volumes of data. This data needs expert evaluation to distinguish between benign and malignant tumors. Enter artificial intelligence (AI), which offers improved accuracy in analyzing large quantities of diagnostic imaging, thereby enhancing the efficiency of healthcare systems. The integration of new AI algorithms, technical advances, and enhanced computer hardware enables the training of diagnostic neural networks. This allows machines to learn from a diverse range of scans, leading to a comprehensive understanding of cancer scanning data. AI software has been evaluated against conventional diagnostic tools used by cancer specialists, and the results show significantly increased precision, making it highly effective in early diagnosis and extended forecasting for various types of cancers. In the realm of breast cancer prognosis, AI systems have demonstrated the potential to surpass human specialists, enabling much earlier diagnosis. Similarly, informatics has developed AI algorithms and deep learning techniques capable of predicting individuals' likelihood of developing lung cancer through low-dose CT analysis. The use of convolutional neural networks (CNNs) has been instrumental in diagnosing the invasion depth of gastric cancer based on gastric endoscopy. KEYWORDS- Cancer, Diagnostic Tests, Artificial Intelligence, Deep Learning Techniques. I. INTRODUCTION It is an evolution that will have a significant impact on the medical field. It holds great promise for improving and enhancing many facets of medical practice. This could ultimately result in better patient outcomes, increased productivity, and revolutionary shifts in the way healthcare is provided. Artificial intelligence (AI) technologies, such as computer vision, natural language processing, and machine learning, allow the analysis of large datasets and provide insights that can guide treatment planning, diagnosis, and clinical decision-making. This promotes more efficient and individualized patient care in addition to quickening the rate of medical research and innovation. Furthermore, the use of AI in healthcare has the potential to address persistent issues like the growing strain on healthcare systems The simulation of human intelligence processes by machines is termed artificial intelligence. It is the theory and development of computer systems capable of performing tasks that require human intelligence, such as recognizing speech, making decisions, and identifying patterns. Artificial Intelligence is a general term that includes a broad range of technologies, including machine learning, deep learning, and natural language processing (NLP). While each has unique strengths and weaknesses, humans and machines can work in tandem to provide and improve healthcare. According to a recent definition provided by the American Medical Association, artificial intelligence in healthcare will be utilized to augment human intelligence rather than to replace it. The American Medical Association's perspective, which has significant ramifications for the application of AI in healthcare, emphasizes the collaboration between humans and machines. Medical Imaging: Algorithms for machine learning are capable of processing vast amounts of data rapidly. Clinical Decision Support (CDS): By giving physicians pertinent information, AI can assist medical professionals in making better decisions. Drug Development: AI can expedite and assist in the identification of new drugs and this saves a lot of time. Artificial Intelligence has the potential to enhance the accuracy of robot-assisted surgery that can make the operation more effective. II. LITERATURE REVIEW A. Digital mammography screening using Standalone AI was as effective as or better than radiologists[1] Nine datasets from multi-reader, multi-case studies, previously utilized for diverse research purposes across seven countries, were gathered. Each dataset comprised digital mammography (DM) exams captured through systems from four different vendors. These exams underwent assessments by multiple radiologists, and the ground truth was established through histopathological analysis or follow-up. In total, there were 2,652 exams (653 malignant) and interpretations by 101 radiologists, resulting in 28,296 independent assessments. An artificial intelligence (AI) system analyzed these exams, providing a suspicion level of cancer presence on a scale from 1 to 10.