How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications

Abstract

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Navigating the Future: A Comprehensive Review of Artificial Intelligence Applications in Gastrointestinal Cancer

“…AI-based diagnostic tools have demonstrated a capacity to augment the accuracy and speed with which complex medical images are interpreted, consequently enhancing the early detection of diseases, including cancer. Leveraging machine learning algorithms, AI systems exhibit the capability to swiftly and precisely analyze medical images, facilitating the identification of early-stage diseases that might pose challenges for traditional detection methods [13]. Notably, AIenabled medical imaging tools contribute to the automation of image analysis, diminishing manual workloads and potentially expediting diagnoses [14].…”

“…These advanced algorithms can analyze vast sets of medical data to identify risks and predict the potential spread of diseases based on symptoms and genetic history [28]. AI-based diagnostic tools play a crucial role in accelerating the interpretation of intricate medical images, leading to enhanced early detection of diseases and ultimately contributing to improved outcomes for patients [13]. For example, AI has proven effective in detecting mammographic abnormalities, showcasing a comparable accuracy to that of radiologists in breast cancer imaging [29].…”

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

“…Using algorithms like GANs and variational autoencoders has proven effective in generating synthetic medical images, such as X-rays, CT scans, and MRIs. These technologies play a role in enhancing the accuracy and robustness of medical imaging systems [42]. Figure 3 depicts the generative components of ML and DL.…”