Understanding Artificial Intelligence in Health Research

Artificial intelligence is increasingly used to support literature review, study planning, coding, data analysis and scientific writing. This level introduces the concepts a health researcher needs to understand these tools and use them appropriately. It explains artificial intelligence, machine learning, deep learning, generative AI and large language models in practical terms, and shows where each may be relevant in a research project. The emphasis is on understanding their capabilities and limitations, while keeping human judgement central to research decisions.

Who this level is for

Researchers, clinicians, epidemiologists, public health professionals, students, research coordinators, data managers, statisticians and other health professionals who want to use AI appropriately in their work. No programming, machine learning or advanced statistics background is required.

What you'll learn

• What artificial intelligence means in the context of health research
• The differences between AI, machine learning, deep learning, generative AI and large language models
• The basic principles behind how generative AI systems produce responses
• Where AI can be used across the research lifecycle
• Common strengths and limitations of AI tools used in research
• Why hallucinations, factual errors and unsupported statements can occur
• Why responsibility for AI outputs remains with the researcher

You finish with: An AI use map for a research project + your personal AI readiness checklist.