Multimodal Large Language Models for Healthcare

Uncover the vital importance of how AI is learning to understand healthcare

Large language models can now do more than generate text. They can connect information, interpret images, retrieve knowledge, and increasingly interact with the world in ways that begin to resemble reasoning.

But what happens when these systems move into healthcare?

On this online course from the University of Glasgow, you’ll examine the next generation of healthcare AI through multimodal large language models (LLMs).

Across four weeks, the course traces how LLMs are evolving from language models into systems capable of understanding complex healthcare information across multiple sources and formats.

Follow the evolution of large language models

Begin by tracing the development of large language models and the ideas that underpin modern generative AI.

You’ll unpack how language models work, where machine learning and LLMs overlap, and why these systems have rapidly transformed healthcare AI and research.

Connect language with knowledge and reasoning

Large language models are powerful, but they can also make mistakes.

Attention then turns to knowledge graphs, fact checking, and methods that strengthen reliability and contextual understanding within AI systems.

You’ll investigate how structured knowledge can work alongside LLMs to support healthcare datasets.

Expand AI beyond language into vision and healthcare practice

Lastly, you’ll explore how these systems move from research into clinical settings, you’ll gain insight into how healthcare AI is evolving - and the questions this transformation raises for the future.

This course is for learners interested in generative AI and healthcare AI with prior knowledge of Python, statistics, and machine learning. It’s suitable for those exploring language models and their future applications in healthcare.

These are online platforms that might be useful for this course: 1. Kaggle code: https://www.kaggle.com/code 2. Google Colab: https://colab.research.google.com/ (optional)

This course is for learners interested in generative AI and healthcare AI with prior knowledge of Python, statistics, and machine learning. It’s suitable for those exploring language models and their future applications in healthcare.

• Synthesise clinical text data and medical records using modern NLP and large language model architectures for healthcare applications.
• Explore foundational principles of natural language processing and transformer models applied to electronic health records.
• Apply multimodal language model workflows to structure, analyze, and interpret complex healthcare datasets.
• Discuss ethical considerations, data privacy, and governance frameworks necessary for deploying AI systems in medical settings.
• Critique contemporary clinical text processing literature and benchmark models to identify strengths, limitations, and practical biases.
• Evaluate the opportunities, performance, and translational challenges of implementing AI models within clinical workflows.