AI Competency Centre Research Webinar - Using Large Language Models to Support Hermeneutic Systematic Review

Are you curious about how generative AI can do more than speed up searches and screening in systematic reviews? Join us for an insightful webinar with Professor Trish Greenhalgh, a world-leading expert in health research methods and evidence synthesis, to explore a bold new approach.

Professor Greenhalgh is a medical doctor and Professor of Primary Care Health Sciences, recognised internationally for her work at the intersection of medicine, social science and digital innovation. Her research spans evidence synthesis, innovation adoption in healthcare, and narrative and interpretive methods — including hermeneutic and meta-narrative reviews. She has authored almost 500 peer-reviewed publications and 16 textbooks, and received numerous honours, including an OBE and fellowship of the UK Academy of Medical Sciences.

In this webinar, Professor Greenhalgh will:

🔍 Introduce a novel use of generative AI (ChatGPT 5.1 research mode) to support hermeneutic systematic review — a review method grounded in detailed textual interpretation and iterative evidence understanding.

📚 Walk through the Mechanism-Informed Narrative Synthesis of Complex Evidence (MINSCE) framework — covering five key stages from search to synthesis — using mask efficacy and respiratory disease transmission as a real case example.

🤖 Discuss where AI excels and where it falls short — including misclassification, mislabelling, confabulation and missed insights — and reflect on how close human reading remains essential to trustworthy hermeneutic synthesis.

This session is ideal for researchers, AI practitioners, methodologists, and anyone interested in how large language models can meaningfully augment deep qualitative and interpretive scholarship.