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Healthcare is becoming increasingly expensive, and developed countries worldwide including òòò½Íøhave ageing populations. With rising healthcare demands and costs, there is a need for innovation to address issues of access, productivity and effectiveness.

At the HM division, our core capabilities include artificial intelligence, machine learning, visual computing, natural language processing, signal processing, robotics and privacy preserving technologies for biomedical applications. We leverage these strengths to produce high-quality research in many untapped areas of healthcare and medicine. Examples include clinician and patient facing solutions for rehabilitation, management of neurological and cardiometabolic disease, and primary care and population health initiatives.

RESTORES: NNI, TTSH, and I2R collaborated to work on the RESTORES trial, which involved the use of spinal stimulator implants to restore movement to patients suffering from lower limb paralysis. ()

LumiHealth: We collaborated with the Health Promotion Board (HPB) to leverage the deep lifestyle and sensor data from their LumiHealth programme to improve understanding of citizens' health needs and inform intervention design. ()

Neeuro: BCI technology developed by I2R was used to develop a complementary home-based attention training programme for children with ADHD. ()

Tetsuyu Healthcare: We collaborated with Tetsuyu to develop the CARES4WOUNDS Wound Management System, an AI-enabled Wound Care Imaging, Assessment, Management and Monitoring System designed to raise productivity and quality in wound care. ()

Apollo: Apollo is a collaboration between SingHealth's National Heart Centre, Duke-NUS Medical School, A*STAR, NUH, and TTSH. This is a national AI project for the purpose of analysing scans of heart arteries, to determine if a patient has cardiovascular disease. ()

Neurostyle: In collaboration with Neurostyle, we designed and developed a brain-computer interface for stroke rehabilitation. ()

3Dgait: This tool, developed as a collaboration between I2R, DxD, and Carecam, is a medical software solution that can provide comprehensive gait analysis and reporting, and is targeted at adults with gait abnormalities undergoing rehabilitation. ()

BC Platforms: Collaborating with I2R, BC Platforms has developed a homomorphic encryption solution that enables the analysis of encrypted data without compromising security. ()

Diabetes Clinic of the Future (DCOF):  DCOF is a collaboration between SingHealth, A*STAR, Duke-NUS Medical School, and the NUS Centre for Behavioral Economics, aimed at developing solutions using data science, AI and digital technology to personalise care and improve outcomes for patients with type 2 diabetes. As part of this programme, we developed smart and interactive AI solutions to enhance individualized treatment and promote behavioural modifications in type 2 diabetes. ()

We regularly host students through projects within our division. Please check links below for opportunities through A*STAR Graduate Academy:

• SINGA
• ARAP
• ARIA
• SIPGA

M. Nambiar et al., “A drug mix and dose decision algorithm for individualized type 2 diabetes management,” npj Digital Medicine, vol. 7, no. 1, pp. 1–12, Sep. 2024, doi: . (Impact Factor 12.4)

E. Wong et al., “The òòò½ÍøNational Precision Medicine Strategy,” Nature Genetics, vol. 55, no. 2, pp. 178–186, Feb. 2023, doi: . (Impact Factor 31.7)

S. Mansour, J. Giles, K. K. Ang, K. P. S. Nair, K. S. Phua, and M. Arvaneh, “Exploring the ability of stroke survivors in using the contralesional hemisphere to control a brain–computer interface,” Scientific Reports, vol. 12, no. 1, p. 16223, Sep. 2022, doi: . (Impact Factor 3.8)

K. Zhou et al., “Memorizing Structure-Texture Correspondence for Image Anomaly Detection,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 6, pp. 2335–2349, Jun. 2022, doi: . (Impact Factor 10.2)

F. Fahimi, S. Dosen, K. K. Ang, N. Mrachacz-Kersting, and C. Guan, “Generative Adversarial Networks-Based Data Augmentation for Brain–Computer Interface,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 9, pp. 4039–4051, Sep. 2021, doi: . (Impact Factor 10.2)

N. Cheng et al., “Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 12, pp. 3339–3351, Dec. 2020, doi: . (Impact Factor 4.8)

W. Zhou et al., “High-Resolution Digital Phenotypes from Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study,” Journal of medical Internet research, vol. 24, no. 7, Jul. 2022, doi: . (Impact Factor 7.4)

B. Premchand et al., “Wearable EEG-Based Brain–Computer Interface for Stress Monitoring,” NeuroSci, vol. 5, no. 4, pp. 407–428, Oct. 2024, doi: . (Impact Factor 1.6)