An Overview of Artificial Intelligence Applications in Radiological Imaging for Bone Fracture Diagnosis.

Fractures are becoming a significant health concern, especially in the aging population, making early detection and accurate management essential for optimal patient outcomes. Standard radiographic imaging methods are the primary diagnostic tool for fractures; however, subtle or complex fractures are often overlooked by radiologists, resulting in delayed treatment and an increased risk of complications. Adding artificial intelligence (AI) to radiology would make diagnoses more accurate, reduce wait times, and make it easier for doctors to make decisions in busy, urgent situations. This literature review discusses contemporary evidence regarding the role of AI in radiology and fracture detection, emphasizing machine learning models, particularly convolutional neural networks (CNNs) and deep neural networks (DNNs). It also explores critical aspects such as clinical workflow, diagnostic efficacy, and the ethical dilemmas associated with the integration of AI technologies in healthcare. The results show that AI systems are highly sensitive in detecting subtle and complex fractures and, in some cases, perform better than radiologists. AI algorithms can also help doctors prioritize urgent cases, speed up reporting, and lower the risk of missing fractures in busy settings like the emergency department. Nonetheless, further research is required before integrating AI into the healthcare system, as challenges often overlooked remain, including data privacy concerns, algorithmic bias, and the need for radiologist oversight to ensure patient safety. This literature also discusses ethical issues, such as how to maintain clinicians' autonomy when using AI as a decision-support tool. In general, using AI in radiology is beneficial when clinicians use it as an additional tool to detect fractures. It can help make diagnoses more accurate, speed up the process, and improve patient outcomes. However, future research should focus on extensive and practical studies, develop strategies to ensure the safe and ethical incorporation of AI into clinical practice, and enhance radiology training in AI. This review highlights the potential benefits of AI in healthcare while emphasizing the need to address the ethical dilemmas and challenges that arise.