Artificial Intelligence (AI) Applications and Challenges in Radiology: A Systematic Review and Thematic Synthesis

Abstract

Background: The integration of Artificial Intelligence (AI) in radiology has significantly transformed the field, enhancing diagnostic accuracy, optimizing workflow efficiencies, and ultimately contributing to improved patient outcomes. Despite these advancements, several challenges may arise. The objective of this study is to systematically review and synthesize papers exploring the applications and challenges of AI in radiology.

Methods: The design of the study is based on a systematic review and synthesis of qualitative papers. Papers are identified through (3) data sources which are searched in the English language from 2017 to 2024. Studies that explored AI applications in radiology and addressed challenges associated with these technologies were eligible for inclusion. Three researchers independently screened the titles and abstracts of papers.  A total of (17) papers were included in the review.

Results: the findings of the systematic review and thematic synthesis showed the transformative impact of AI across various domains of radiology. From enhancing diagnostic accuracy and workflow efficiency to improving patient outcomes and safety. On the other hand, it is shown that there are challenges associated with integrating AI into radiology from transparency and trust issues to data quality and algorithm robustness, the successful implementation of AI in radiology hinges on collaboration among stakeholders, ongoing education and training, and the establishment of clear regulatory frameworks.

Conclusion: As the field of radiology continues to evolve, ongoing research and collaboration will be essential to ensure that AI technologies are effectively integrated into clinical practice. By proactively addressing AI-related challenges, the radiology field can harness the full potential of AI, ultimately improving patient outcomes and advancing the quality of care in healthcare settings.