Integrating AI with Medical Imaging in the Healthcare Domain

Medical imaging is transforming the landscape of oncology by playing a critical role in the process of cancer detection and therapeutic approaches. Medical experts may visualize the internal architecture of the body with unmatched precision using modern technologies such as MRI, X-rays, CT scans, Ultrasound and PET scans. Imaging techniques in cancer identification allow for the early detection of aberrant tissue growth, assisting in the prompt diagnosis and characterization of various tumours. Medical imaging also aids in treatment planning by providing detailed information regarding the size, location, and nature of malignant lesions. The introduction of machine learning in medical imaging has been a transformational force, as it changes how healthcare workers evaluate and interpret complex data. Deep learning, specifically, has proven critical in tasks such as image segmentation, classification, and detection, considerably improving diagnostic accuracy. Deep learning algorithms, particularly convolutional neural networks (CNNs), excel at learning extensive patterns and characteristics from large datasets, allowing them to identify subtle abnormalities in medical images that the human eye may not pick up on. This technology has considerably increased the speed and precision of diagnostic processes, enabling earlier and more precise diagnosis of diseases, namely cancer. Deep learning integration in various regions of medical imaging represents a paradigm shift, enabling innovations with the potential to reshape the healthcare field, leading to more efficient and effective patient care in the long run, as will be further discussed. Speaker(s): Maeashah, Room: CAR 09, Bldg: The Carlton Cinema (CAR) , 20 Carlton Street, Toronto, Ontario, Canada