Integration of AI in Medical Imaging: Enhancing Diagnostic Accuracy and Workflow Efficiency

Abstract

The application of AI in medical imaging has gained momentum over the years and is revolutionizing the quality of health facilities' diagnosis quality and increasing the efficiency of work experience. Advanced technologies such as ML and DL are used to study big volumes of medical data more effectively than conventional approaches. This paper discusses the use of AI in medical imaging with particular reference to diagnostic precision and efficiency. A number of techniques provided by machine learning, such as pattern identification, decrease the rate of misdiagnosis and offer clinicians means to make more accurate diagnoses in a timely manner. Furthermore, applying artificial intelligence in image recognition and initial evaluation takes a large load on radiologists and can help them concentrate on difficult cases only. Data privacy is another challenge; not all the data can be shared publicly and accessed by anyone, Algorithmic transparency, and Bias. This paper also brings into focus the further prospects of AI use in medical imaging, as the deployment of this technology is expected to develop superior diagnosis techniques in the future. In conclusion, the use of AI in medical imaging has the potential to improve health outcomes, cut operation costs, and increase efficiency in medical practices internationally.