Marcel Nickel

Keywords: Image acquisition: Reconstruction, Image acquisition: Machine learning

With their capability to push MRI towards higher resolution, higher signal-to-noise and/or shorter acquisition time, AI-based reconstruction and image enhancement techniques have quickly transitioned into clinical routine. This educational talk will illustrate how conventional MR reconstructions can be combined with trainable components and outline how these architectures can be trained for prospective use in clinical applications. Based on examples the benefits, limitations and on-going developments will be discussed.

Ito Rintaro

Keywords: Transferable skills: Software engineering

Large Language Models (LLMs) are increasingly utilized in diagnostic imaging, offering capabilities in processing text and images, and handling composite modalities. They assist in creating accurate diagnostic reports, interpreting images, and integrating clinical information. However, challenges include ensuring output reliability, addressing biases, and tackling ethical and privacy concerns. Future improvements involve continuous training, ethical standards, interdisciplinary collaboration, and regulatory validation. These steps aim to enhance LLMs' efficiency and accuracy in diagnostic imaging while addressing concerns about reproducibility, prompt handling, and ethical considerations.

Indrani Bhattacharya