Another productive CAIMed Quarterly meeting is in the books! It was a fantastic opportunity to reflect on our progress, foster collaboration, and plan for the future.

On the technical front, we explored opportunities with Large Language Models (LLMs) in genomics, DNA foundation models, cancer type prediction, and AI-driven integration of multimodal OMICS and clinical data for an enhanced understanding of post-acute infection syndromes.

We also discussed the integration of hybrid AI and semantic methods like Retrieval-Augmented Generation (RAG). These approaches not only leverage the power of foundation models but also enhance prediction accuracy through the incorporation of semantics. LLMs, particularly those based on transformer architectures, are capable of handling both sequential (e.g., DNA sequences) and non-sequential data (e.g., single-cell RNAseq), making them promising tools for a variety of downstream tasks in genomics.

Topics such as new publications, projects and the shared use of resources were also the focus of the quarterly meeting. The interdisciplinary teamwork and innovation within CAIMed encourages us to drive forward the possibilities of AI in medicine.

We look forward to further research progress and working together with our partners!