JARVISDHL: Transforming Chronic Care for Diabetes, Hypertension and HyperLipidemia (DHL) with AI


(Left to right) Dr Janil Puthucheary, Prof Tan Ngiap Chuan, Prof Lee Mong Li, Prof Ng See Kiong, Prof Wynne Hsu, Prof Marcus Ong Eng Hock, Prof Wong Tien-yi

The team will develop an AI system called JARVISDHL to gather local healthcare data in order to create AI algorithms and models. This will facilitate practice of evidence-based personalised care and shared-decision making by primary care physicians. 

The project aims to integrate multiple solutions into a consolidated AI platform which can be used to improve the 3H care delivery process.

Click here to read about the team’s progress. 

  • Principal Investigators and Collaborators
  • Benefits to Primary Care Team and End Users
  • Recent Achievements

Principal Investigators & Collaborators

Lead Principal Investigator: Prof Wynne Hsu (NUS)

Co-Principal Investigators:

Professor Ng See-Kiong (NUS)

Professor Lee Mong Li (NUS)

Associate Professor Chee Yong Chan (NUS)

Professor Wong Tien-Yin (SingHealth)

Professor Marcus Ong Eng Hock (SingHealth)

Associate Professor Tan Ngiap Chuan (SingHealth)

Dr Teh Ming Ming (SingHealth)

Adjunct Associate Professor Yeo Khung Keong (SingHealth)

Host Institution: National University of Singapore (NUS)

Partner Institution(s): SingHealth Group (SingHealth)

Benefits to Primary Care Team

Predictive Care for 3H Patients

Evidence-based Personalised Care

Right Site-care of Patients

Enable predictive care for 3H patients through early screening and risk stratification.

Facilitate practice of evidence-based personalised care and shared-decision making by primary care physicians.

Physicians will get to the right site-care of patients rather than the usual reactive event-driven sequential referral model, thus spending spend less physical time in healthcare institutions, optimizing healthcare utilization. For example, JARVIS will recommend evidence-based treatment options, quantify personalized treatment benefits and risk of complications, adapt treatment regimen based on lifestyle, alleviate anxiety over perceived side effects, and support holistic clinical decision-making.

Benefits to End-users

Patients are empowered to take ownership of their healthcare journey beyond the clinical visits through the use of technologies for patient education and self-care.


  • 3 conference publications in international conferences [Computer Vision and Pattern Recognition (CVPR 2019), IEEE International Conference on Image Processing (ICIP 2019) and IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2019)] 
  • Project requested to be featured in the American Diabetes Association (ADA)'s Thought Leadership Film series