Rick Goh

Adj Assoc Prof Rick Goh

Associate Professor (Adj.) Rick Goh is Director of Computing & Intelligence at A*STAR’s Institute of High Performance Computing (IHPC), Associate Professor (Adj.) at Duke-NUS Medical School, Co-Director of A*STAR-EVYD Joint Lab, Senior Principal Investigator (Adj.) at Singapore Eye Research Institute (SERI), and Co-Director of SERI-IHPC Joint Lab. He co-organised the inaugural AI Health Summit in 2022 with SingHealth and Ministry of Health. 

Adj A/Prof Goh has co-authored 150+ peer-reviewed papers in renowned clinical journals such as Nature Aging, Nature Genetics, The Lancet Digital Health, and other top-tier AI and computing journals and conferences such as Nature Machine Intelligence, Nature Communications, IEEE TPAMI, TNNLS, TPDS, Computers, Cybernetics, Transactions on Medical Imaging, Medical Image Analysis, CVPR, CACM, AAAI, IJCAI, MICCAI, and Supercomputing Conference (SC). 

He has recently secured multiple highly-competitive AI Singapore Tech Challenges and Grand Challenge grants, received best paper awards, Healthcare AI project awards, and has been recognised with a National Award (COVID-19) Commendation Medal. 

Presentation Synopsis
HE 6 - AI and High-Performance Computing for Smarter, Safer, and Scalable Healthcare Solutions
As Singapore advances its Smart Nation and Healthier SG agendas, engineering innovation at the intersection of AI, computing, and healthcare has become critical to address evolving healthcare challenges. This presentation will showcase A*STAR IHPC's latest R&D efforts in applying AI and high-performance computing to enable scalable, safe, and impactful healthcare technologies.

Key highlights include:

• AI-driven innovations in ophthalmology, radiology, dermatology, and home-based care to enhance early detection, triaging, and personalized disease management. 
• The development of medical foundation models, including universal retinal models and multi-specialty vision-language models, designed to improve diagnostic accuracy and workflow efficiency. 
• Robust and privacy-preserving AI approaches, including federated learning platforms and AI safety research, ensuring trustworthy deployment in real-world clinical settings. 
• The critical role of high-performance and green computing in accelerating AI model training, large-scale medical data processing, and next-generation digital health platforms. 

The presentation will share technical insights, real-world impact, and lessons from national and international collaborations, offering healthcare engineers and innovators practical perspectives on driving safe, scalable AI adoption in healthcare.

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