Artificial intelligence (AI) in medicine is a fast-growing field. The rise of deep learning algorithms, such as convolutional neural networks (CNNs), offers fascinating perspectives for the automation of medical image analysis.
From basic sorting algorithms to sophisticated neural networks, AI and its offspring continue to generate buzz throughout medicine, business, academia, and the media.
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization, and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics.
This is owing to recent advances in AI research, the massive amounts of digital data now available to train algorithms and modern, powerful computational hardware. Deep learning methods have been able to defeat humans in the strategy board game of Go, an achievement that was previously thought to be decades away given the highly complex game space and massive number of potential moves. Following the trend towards a human-level general AI, researchers predict that AI will automate many tasks, including translating languages, writing best-selling books and performing surgery — all within the coming decades.
The aim of the conference is to present the advantages of AI usage in radiology and medicine.
This conference is designed for radiologists, other health care professionals, IT specialists as well as for hospital managers.
Zeleňák K. and organizing team