ARTIFICIAL INTELLIGENCE IN DIAGNOSTIC IMAGING: IMPACTS ON MULTIPROFESSIONAL CLINICAL DECISION-MAKING AND PATIENT SAFETY

Abstract

The incorporation of artificial intelligence (AI) into diagnostic imaging has significantly transformed contemporary clinical practice, particularly in supporting the interpretation of radiological examinations and clinical decision-making. This study aimed to analyze the contributions of artificial intelligence to diagnostic imaging and discuss its impacts on multiprofessional practice and patient safety. This qualitative study was conducted through an integrative literature review using the databases PubMed/MEDLINE, Scopus, SciELO, Virtual Health Library (BVS), and Google Scholar, considering publications between 2018 and 2025. The findings indicate that machine learning and deep learning algorithms have improved diagnostic accuracy, accelerated image analysis, and supported safer clinical decision-making processes. Furthermore, the integration of artificial intelligence into healthcare practice strengthens multiprofessional collaboration, enabling radiologists, physicians, nurses, pharmacists, physiotherapists, nutritionists, and dentists to use automated image analysis to guide more effective therapeutic interventions. However, challenges related to algorithm validation, professional training, and ethical and regulatory issues still need to be addressed. It is concluded that artificial intelligence applied to diagnostic imaging represents a promising tool to improve diagnostic accuracy, strengthen multiprofessional clinical decision-making, and enhance patient safety in the context of digital health.