ESR/EORTC - Next-generation imaging: clinical trials and AI

1. To understand the foundations of language modelling.
2. To learn about application scenarios of large language models in radiology.
3. To learn about current and future developments in multimodal deep learning.

1. To understand the importance of careful validation of AI tools in the context of data drift and data shift.
2. To discuss potential approaches to post-market surveillance of AI tools.
3. To learn about potential pitfalls in human-machine interaction and their impact on AI tools.

1. To learn about the potentials and caveats of AI in clinical HI.
2. To appreciate the promise of AI and the responsibilities of the HI user community.
3. To understand the role of AI in the entire workflow of HI (from data acquisition to data corrections and quantification, as well as data handling and prediction models building on HI).

1. To learn about the potential benefits of AI for oncological follow-up.
2. To understand the role of AI in the workflow of tumour monitoring.
3. To discuss challenges and potential pitfalls when using AI in oncological patients.

1. To acknowledge why the quality of data collected plays a major role for patients on the one hand but for all stakeholders on the other hand regarding environment, costs, optimisation, evaluation, usability and acceptance, safety, and treatment progress.
2. To understand what we can learn and copy from other industries successfully implementing AI and deep learning.
3. To address misleading AI use of patients like Google and ChatGPT and to use tools to build awareness to avoid this.