Evaluation of Diagnostic Accuracy of Artificial Intelligence Algorithms in Digital Mammograms for Early Detection of Breast Cancer

Abstract

The objective of this study is to review the diagnostic accuracy of artificial intelligence (AI) algorithms in digital mammograms and to identify the challenges and limitations associated with this approach. This study is an integrative literature review that analyzes the performance of AI algorithms for the early detection of breast cancer compared to the investigation and management by healthcare professionals. The aim is to provide relevant insights for clinical practice and to explore future perspectives and potential technological advancements in early breast cancer detection.The research was conducted using the databases Scielo, BVS – Virtual Health Library, and PubMed, employing descriptors such as "Breast Neoplasms," "Mammography," "Artificial Intelligence," and "Early Diagnosis." Articles published between 2020 and 2024 in Portuguese or English, with full access and relevance to the study, were included. Paid or incomplete articles, dissertations, theses, and materials outside the specified timeframe were excluded. Technological advancements and the application of AI in healthcare have sparked interest in the potential to improve diagnostic efficiency. Early detection can directly impact patient survival and quality of life. In this context, the application of AI algorithms in digital mammograms represents a promising advancement but also challenges traditional paradigms and practices in healthcare.