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Glasgow_Tutorial

Repository for University of Glasgow tutorial material and website


Multimodal Large Language Models for Healthcare

Recent advances in medical multimodal large language models (MLLMs) have enabled integration of diverse data types—clinical reports, images, and knowledge graphs—within unified, interpretable frameworks for healthcare AI. This tutorial covers concepts, real-world applications, and hands-on techniques to adapt these models for use in clinical practice.


Schedule

Time Topic Speaker Duration
10:00am Era of Large Language Models
Impact and limitations of LLMs
Open-source models
Honghan Wu 30 mins
10:30am Error Detection & Fact Checking with Knowledge Graphs
Knowledge graph integration
Enhancing MLLMs
Honghan Wu 30 mins
11:00am Break 15 mins
11:15am Integrating LLMs & Knowledge Graphs for EHRs
EHRs, contextual reasoning
Yusuf Abdulle 30 mins
11:45am Unifying Multimodal Clinical & Genomic Data
Knowledge graph-driven precision medicine
Yusuf Abdulle 30 mins
12:15pm Lunch Break 60 mins
13:15pm Expanding LLMs to Vision: Multimodal LLMs Yunsoo Kim 30 mins
13:45pm Multimodal LLMs for Radiology
Data construction
Visual instruction tuning
Reasoning enhancement
Applications: report generation, diagnostics, error checking
Jinge Wu 90 mins
15:15pm Break 15 mins
15:30pm Multimodal LLMs for Pathology
Patch and WSI models
Report generation & metrics
Yunsoo Kim 30 mins

Presenters

  • Yunsoo Kim PhD candidate, UCL; former senior NLP researcher, LG Chem. Organizer at HealTac2024, MICCAI2024/2025. Focus: human-expert enhancement in MLLMs.
  • Jinge Wu Research fellow, IHI UCL; specializes in radiology MLLMs, published in Nature Biomedical Engineering, Nature Reviews Bioengineering, The Lancet Digital Health.
  • Yusuf Abdulle PhD candidate, King's College London; researches EHRs, knowledge graphs, imaging, genomics in ALS/MND, NHS, and startups.
  • Prof. Honghan Wu Professor of Health Informatics & AI, University of Glasgow. Co-leads SHW Data Science, leads UCL KnowLab. Expertise: health informatics and LLMs.

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