Review of Innovations and Applications of Artificial Intelligence in Medical Image Processing and Clinical Processes
Code: G-1604
Authors: Ghazaleh Shafei Digehsara * ℗, Ghaniyeh Shafei Digehsara, Maryam Hajiee
Schedule: Not Scheduled!
Tag: Biomedical Signal Processing
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Abstract:
Abstract
Background and aims: Artificial intelligence (AI) has emerged as a transformative tool in medical imaging and clinical processes, offering enhanced capabilities for diagnostics, treatment planning, and patient monitoring. With the increasing complexity of medical data, AI techniques enable more accurate and efficient processing of medical images and clinical information. This study aims to explore the innovations and applications of AI in medical image processing and clinical workflows, emphasizing how AI enhances healthcare delivery. Method: This review investigates the various AI techniques applied to medical imaging, such as deep learning models, convolutional neural networks (CNNs), and generative adversarial networks (GANs). Additionally, we examine AI's integration into clinical processes, including diagnosis, treatment planning, prognosis prediction, and patient monitoring. A comprehensive literature review was conducted, focusing on both traditional and cutting-edge AI methodologies and their impact on healthcare outcomes. Results: The study identifies key AI-driven innovations in medical imaging, such as automatic detection of diseases (e.g., cancer, neurological disorders), image quality enhancement, and predictive models for disease progression. Furthermore, AI has demonstrated significant benefits in clinical processes, including personalized treatment plans, early detection of health conditions, and improved patient monitoring. The ability of AI to analyze vast datasets quickly and accurately has proven to reduce diagnostic errors and enhance patient care. Conclusion: AI applications in medical imaging and clinical processes are revolutionizing healthcare by improving diagnostic accuracy, optimizing treatment approaches, and enhancing patient care. While many AI tools show promising results, challenges remain in terms of clinical integration, data privacy, and ensuring algorithm transparency. Future research should focus on refining these technologies, enhancing interoperability, and addressing ethical considerations to fully realize AI’s potential in healthcare.
Keywords
Artificial Intelligence, Medical Imaging, Clinical Processes
