TIA Centre Events

An agentic framework for autonomous scientific discovery in cancer pathology:
SPARK is an agentic AI framework that autonomously generates biologically-driven concepts for cancer pathology. Across 18 cohorts and more than 5,400 patients, SPARK identified clinically relevant concepts associated with prognosis, pathology variables and predictive biomarkers.

Safety and Security of Large Language Models in Healthcare.
Large language models (LLMs) are rapidly entering clinical practice. Drawing on insights from a recent Nature Review, Germany’s largest survey of clinician AI chatbot use (>4,000 respondents), and real-world deployment experience, Dr Jan Clusmann explores the opportunities, risks, and readiness of LLMs for healthcare.

Pathology Foundation Models-driven Agentic AI for Precision Oncology.
With the deep integration of AI and digital pathology, large pathology models become the foundation backbones in computational pathology for precise cancer diagnosis and treatment. This talk will systematically explore the cutting-edge advancements of large pathological models in precise cancer diagnosis, molecular subtyping prediction, treatment response assessment, and prognostic analysis, revealing the breakthrough potential in achieving "micro-meso-macro" analysis through multimodal data integration (such as whole-slice images, imaging, genomics, and clinical information). We will further explore how large pathology models-driven agentic AI are transforming the paradigm in precision oncology and beyond.

Explainable Artificial Intelligence (XAI) in Computational Pathology.

Computational pathology models are increasingly used for diagnosis, prognosis, and treatment prediction, yet many remain difficult to interpret. Drawing on a recent MICCAI SIG-CompPath review, this talk presents a pathology-focused framework for explainable AI, including a structured taxonomy of XAI methods and practical guidance for linking clinical questions to appropriate interpretability approaches.