Summary
This lecture covered the rapidly changing landscape of artificial intelligence (AI) and its transformative role in health research. It begins by explaining key AI concepts such as machine learning, deep learning, large language models (LLMs), and generative methods, then moves to real-world clinical research applications. Practical examples include automated chart summaries, natural language queries of clinical databases, and diagnostic image analysis. The lecture also discusses the essential infrastructure and deployment options—whether in the cloud, on-premises, or hybrid—needed for effective AI integration. Using case studies on COVID-19 and chronic disease management, it shows how AI has already enhanced clinical workflows, triage, and population health analytics. The session also covers prompting strategies, domain-specific fine-tuning, and the ethical and competency issues necessary for responsible AI use in healthcare research.