THE IMPACT OF ARTIFICIAL INTELLIGENCE ON IMAGE EXAM INTERPRETATION AND CLINICAL RADIOLOGICAL PRACTICE

Abstract

Introduction: In recent years, artificial intelligence (AI) has emerged as a technology with a significant impact on medicine, particularly in radiology. The development of machine learning algorithms has led to substantial advances in medical image interpretation, resulting in more precise and efficient analyses. These advancements are transforming radiological clinical practice, improving diagnostic accuracy, and facilitating early disease detection. Objective: The aim of this study is to critically assess the impact of AI in radiology, focusing on its diagnostic effectiveness, workflow optimization in clinical settings, and the ethical and practical challenges related to its implementation. Method: A systematic literature review was conducted using databases such as PubMed and Scopus, covering studies published between 2019 and 2024, with strict inclusion and exclusion criteria. Results: The results indicate that AI has had a positive impact, improving diagnostic accuracy in radiological modalities such as tomography, magnetic resonance, and X-rays. Additionally, it has contributed to workflow optimization by reducing analysis time and aiding in the triage of complex cases. However, significant challenges remain, including data biases used to train the algorithms and lack of transparency in their decision-making processes, which may create distrust and limit their wider adoption. Conclusion: AI has the potential to revolutionize radiology by increasing efficiency and diagnostic accuracy. However, improvements in data representativeness and regulatory frameworks are essential to ensure safe and effective implementation in clinical practice.