INTEGRATION OF ARTIFICIAL INTELLIGENCE AND ROBOTICS IN MODERN RADIODIAGNOSIS: ENHANCING ACCURACY, EFFICIENCY, AND CLINICAL OUTCOMES

Abstract

The integration of Artificial Intelligence (AI) and robotics into radiodiagnosis is rapidly transforming the landscape of diagnostic medicine. This study aims to evaluate their impact on the accuracy, efficiency, and diagnostic capabilities of modern radiodiagnostic procedures through a comprehensive content analysis of 25 peer-reviewed articles published between 2019 and 2025. Using qualitative coding methods, the research examined thematic patterns related to image interpretation, workflow automation, clinical applications, and ethical considerations. The literature review revealed that AI significantly enhances diagnostic precision by employing deep learning algorithms to detect anomalies such as tumors and fractures, often outperforming traditional methods. Robotic systems further improve procedural accuracy in interventional radiology, including CT- and PET-guided biopsies, and teleultrasound. These technologies collectively improve workflow efficiency by automating repetitive tasks, optimizing image analysis, and reducing turnaround times. Results demonstrated notable improvements in clinical outcomes, accessibility in underserved regions, and radiologist support. However, several challenges were identified, including limited clinician familiarity with AI tools, regulatory ambiguities, ethical concerns regarding data use and algorithmic bias, and infrastructural barriers. Despite these limitations, physician interest in AI-based education and systems integration is growing. The discussion emphasized the need for strategic implementation that includes training, interdisciplinary collaboration, and policy development to ensure ethical and effective use. The findings conclude that AI and robotics not only enhance accuracy and efficiency but also expand the potential of diagnostic capabilities in modern radiology. In conclusion, while AI and robotics have already made a significant impact, their broader adoption in clinical settings will depend on overcoming ethical, educational, and logistical hurdles. Continued research and collaboration are essential to fully realize their potential in delivering precise, efficient, and equitable radiodiagnostic services. This study provides a foundation for future innovations and informed integration in diagnostic radiology.