A Scoping Review on the Applications of Artificial Intelligence in Diagnostic Care

Background: Artificial intelligence (AI) is emerging as a promising tool to enhance diagnostic care processes throughout various clinical domains. The use of AI is enhancing diagnostic accuracy through advancements in machine learning and deep learning. Therefore, the aim of this scoping review is to assess the current utilization of AI in diagnostic healthcare services, aiming to identify prevalent themes, trends, and existing gaps in the literature. Methodology: This scoping review uses a structured approach using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA) Checklist. A systematic literature search was conducted on PubMed, utilizing keywords including “artificial intelligence,” “machine learning,” “deep learning,” “diagnostic healthcare,” “medical diagnostics,” “diagnostic accuracy,” “radiology,” and “pathology.” The review aims to answer the population, intervention, comparison, and outcome (PICO) question: “Do hospitals that implement artificial intelligence technologies during diagnostic service-line patient care processes experience quality care improvements compared to hospitals that do not use such technologies?” Conclusion: The study draws conclusions based on evidence that reveals significant promise of AI improving diagnostic accuracy and prognostic predictions across various imaging applications. These findings highlight the evolving landscape of AI in diagnostic care, advocating for rigorous validation and interdisciplinary collaboration to ensure effective clinical integration and maximize quality care outcomes. Future research is needed monitor and effectively implement AI across various clinical settings. Key words: artificial intelligence, diagnostic care, scoping review