Cambridge AI in Medicine Seminar Series - July 2026

Join us for the Cambridge AI in Medicine Seminar Series, hosted by the Cancer Research UK Cambridge Centre and the Department of Radiology at Addenbrooke's. This series brings together leading experts to explore cutting-edge AI applications in healthcare - from disease diagnosis to drug discovery. It's a unique opportunity for researchers, practitioners, and students to stay at the forefront of AI innovations and engage in discussions shaping the future of AI in healthcare.

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. 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

Mengling “Mornin” Feng is an Associate Professor at the National University of Singapore (NUS), where he directs the AI for Public Health (AI4PH) Programme and leads the Biostatistics, Modelling, AI and Data Analytics (B.MAD) domain at the Saw Swee Hock School of Public Health. His work centers on healthcare AI, with interests spanning medical imaging, treatment recommendation systems, and clinical text processing. He earned his PhD from Nanyang Technological University and completed postdoctoral training at Harvard-MIT. Since joining NUS in 2017, he has secured over SGD 10 million in research and translation funding, published nearly 200 papers, received more than 15,000 citations, and was recognized among the world’s top 2% most-cited scientists in 2025.

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

Dr He Kai earned his PhD from the School of Computer Science and Technology, Xi’an Jiaotong University, under the supervision of Prof. Li Chen (recipient of China’s Young Thousand Talents Award), and completed a research visit at Nanyang Technological University with Prof. Erik Cambria (IEEE Fellow). He is currently a postdoctoral researcher at the National University of Singapore, School of Public Health, specializing in medical artificial intelligence and natural language processing (NLP). His work includes two ESI Highly Cited Papers, and a Best Paper Award in IEEE Transactions on Affective Computing. At present, He serves as AE for IEEE Transactions on Affective Computing and Health Data Science.

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.

This is a hybrid event so you can also join via Zoom:

https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09

Meeting ID: 990 5046 7573 and Passcode: 617729

We look forward to your participation! If you are interested in getting involved and presenting your work, please email Ines Machado at [email protected]

For more information about this seminar series, see: https://www.integratedcancermedicine.org/research/cambridge-medai-seminar-series/

Know what’s Happening Next — before everyone else does.

Get App

Host or Publisher Cancer Research UK Cambridge Centre