TIA Centre » TIA Centre Events

09/11 2pm-3pm: TIA Centre Seminar Series - Dr Jan Clusmann, TU Dresden, Germany

Lucy Fox — Wed, 14 Oct 2026 16:49:52 GMT

When: 14:00 - 15:00, Mon, 09 Nov '26

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.

16/11 2pm-3pm: TIA Centre Seminar Series - Dr Hao Chen, The Hong Kong University of Science and Technology, China

Lucy Fox — Wed, 14 Oct 2026 16:52:51 GMT

When: 14:00 - 15:00, Mon, 16 Nov '26

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.

30/11 2pm-3pm: TIA Centre Seminar Series - Dr Frédérique Meeuwsen, Radboud UMC, The Netherlands

Lucy Fox — Wed, 14 Oct 2026 16:54:20 GMT

When: 14:00 - 15:00, Mon, 30 Nov '26

07/12 2pm-3pm: TIA Centre Seminar Series - Shubham Innani, Indiana University School of Medicine, USA

Lucy Fox — Wed, 14 Oct 2026 16:55:46 GMT

When: 14:00 - 15:00, Mon, 07 Dec '26

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.