Advancements in AI-driven diagnostic radiology: Enhancing accuracy and efficiency

Abstract

Background: Healthcare delivery has transformed significantly with the integration of clinical decision support systems (CDS) and medical imaging. Convolutional neural networks (CNNs), a type of artificial intelligence (AI) algorithm, have exhibited remarkable accuracy in discerning intricate patterns and anomalies within medical images, surpassing human capability. Aim: This study aims to explore the impact of AI augmentation on diagnostic tasks, focusing on enhancing sensitivity, accuracy, and interrater agreement across various medical conditions. Additionally, it seeks to investigate how AI simplifies complex processes and integrates with existing technologies, extending its role in CDS systems beyond diagnostic accuracy. Methods: The research examines the effectiveness of AI in interpreting CT imaging and diagnosis. Furthermore, it assesses the integration of AI with radiology to enhance the detection of cerebral hemorrhages on head CT scans in time-pressed clinical settings. The research was performed using search engines such as google scholar and Pubmed. Results: The findings indicate that AI augmentation significantly enhances diagnostic capabilities, improves physician confidence, reduces interpretation time, and optimizes workflow efficiency. AI not only improves accuracy but also simplifies processes, thereby revolutionizing healthcare delivery. Conclusion: As artificial intelligence continues to evolve, its revolutionary potential in healthcare becomes increasingly evident.