This month’s seminar will be held on Thursday 9 July 2026, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom.
A light lunch from Aromi will be served from 11:45.
This is the Eventbrite link to sign up.
The event will feature the following talks:
When AI meets health and public health: from computer vision to LLMs to agentic AIs – Mengling ‘Mornin’ Feng, Associate Professor at National University of Singapore, Director of AI for Public Health Center
Abstract: The rapid evolution of artificial intelligence is reshaping both clinical medicine and public health at an unprecedented pace. This talk traces our lab’s development journey of AI in healthcare — from early breakthroughs in computer vision for medical imaging analysis, through the transformative potential of large language models (LLMs) in clinical text processing and decision support, to the emerging frontier of agentic AI systems capable of autonomous reasoning and action in complex healthcare environments. Drawing on real-world research and applications, we explore how each wave of AI innovation has unlocked new possibilities for disease detection, treatment recommendation, and population health management. We also examine the unique challenges that arise as AI systems become more autonomous, including issues of trust, safety, and equity in diverse healthcare settings.
Building Closed-Loop LLM Systems for Scalable Mental Health Support – Kai He, Senior Research Fellow, Saw Swee Hock School of Public Health, National University of Singapore
Abstract: Mental health systems worldwide are under growing strain, with increasing demand for early support and limited specialist capacity. While large language models (LLMs) have shown promise in conversational mental health applications, most existing systems operate as static chatbots without structured assessment, longitudinal monitoring, or calibrated escalation mechanisms. This talk presents a closed-loop LLM framework designed to support scalable mental health care rather than isolated conversational assistance. The system integrates empathetic dialogue generation with continuous state assessment, structured rubric-based evaluation, and reinforcement-driven improvement. A multi-agent architecture enables iterative feedback between support generation and risk evaluation, forming a dynamic loop that mirrors stepped-care principles in public health.
