THE IMPACT OF ARTIFICIAL INTELLIGENCE ON RADIOLOGY: ADVANCES IN DIAGNOSTIC EFFICIENCY AND TREATMENT PERSONALIZATION

Abstract

Introduction: The European Society of Radiology (ESR) defines Artificial Intelligence (AI) as tools and programs that emulate human cognitive functions. The intersection between technology and health has promoted important advances, mainly through artificial intelligence (AI) in radiology. The study explores how AI can optimize medical diagnoses, highlighting its accuracy in image analysis, in a scenario of increased tests and workload of radiologists. The systematic review evaluates the impact of AI on the DICOM standard, in order to improve the detection of lesions and clinical efficiency. The article also discusses the evolution of the profession of Radiology Technician, emphasizing AI as support, not replacement, of health professionals. Objective: The objective of this article is a systematic review of studies carried out in order to understand the applications of artificial intelligence (AI) in radiology. Ensuring the analysis of possible impacts and contributions, enabling the integration of the tool as an ally to the various health fields. Methodology: integrative review with systematic search in the databases PubMed, Virtual Health Library (BVS-Bireme) and Scientific Electronic Library Online (SciELO-Brazil), using descriptors such as "Artificial Intelligence in medicine", "AI and imaging" and "Radiology". Original articles in English and Portuguese were included, covering case reports, descriptive studies, clinical trials and review articles, published between 2019 and 2024. This time interval was selected to ensure the inclusion of recent and updated research. Review articles with more than five years of publication, those with methodologies inadequate to the purpose of the study and articles in languages not specified in the inclusion criteria were excluded. Results: The results of the article highlight the application of Artificial Intelligence (AI) in radiology, showing its contribution to diagnostic accuracy, optimization of workflows and customization of treatments. The research addresses the scientific validation of AI products, ethical and regulatory challenges, as well as operational issues, such as the automation of repetitive tasks and the early detection of critical findings. Although AI has shown significant advances in areas such as oncology and pediatric radiology, the need for more robust databases and overcoming technical limitations remain challenges for broad clinical implementation. Discussion: In radiology, AI has revolutionized the diagnosis and treatment of diseases, standing out for its ability to process and analyze large volumes of data to interpret medical images. It is especially effective in the early detection of diseases, identifying patterns in images with high accuracy and allowing earlier diagnoses and more effective treatments. In addition, AI reduces the time of reading and interpreting exams by automating repetitive tasks, optimizing the workflow of radiologists. Another significant advance is the customization of treatment, with algorithms that analyze clinical and genetic data to select more effective therapies. However, the integration of AI faces ethical challenges, such as data protection and acceptance by radiologists. Collaboration between health professionals, researchers and policymakers is crucial to ensure the safe and effective implementation of AI in radiology. Conclusion: Artificial Intelligence has revolutionized radiology, providing faster and more accurate diagnoses, especially in early stages of diseases. In addition to optimizing the workflow of radiologists by automating repetitive tasks, AI assists in the customization of treatments, analyzing clinical data and patient images. This results in greater effectiveness of care and reduced costs associated with ineffective interventions. With continuous evolution, AI becomes an essential tool to improve clinical results and efficiency in diagnostic processes.