Keywords
Abstract
The integration of AI into medical imaging has gained traction over recent years, transforming the diagnostic capabilities of healthcare facilities and enhancing workflow efficiency. Cutting-edge technologies such as machine learning (ML) and deep learning (DL) enable a more effective analysis of extensive medical data compared to traditional methods. This paper examines the application of AI in medical imaging, emphasizing its impact on diagnostic accuracy and operational efficiency. Various machine learning techniques, including pattern recognition, help reduce misdiagnosis rates and provide healthcare professionals with tools for more accurate and timely diagnosis. Additionally, employing artificial intelligence in image analysis and preliminary evaluations alleviates significant burdens on radiologists, allowing them to focus on more complex cases. However, the use of AI in medical imaging also presents challenges, such as concerns over data privacy, limited public access to certain data, the need for algorithmic transparency, and the potential for bias. This paper also highlights the future possibilities for AI application in medical imaging, as the implementation of this technology is anticipated to yield enhanced diagnostic methodologies moving forward. In summary, incorporating AI into medical imaging holds promise for improving health outcomes, reducing operational costs, and boosting efficiency in medical practices worldwide.