Singapore’s public sector organizations are turning to cloud-based artificial intelligence (AI) and machine learning to address the nation’s complex challenges.
Amazon Web Services (AWS) serves as a force multiplier for these agencies by providing access to high performance computing, serverless architecture, and advanced generative AI tools.
These partnerships have enabled the agency to move from idea generation to minimum viable product (MVP). With the integration of AWS technology, public services are also moving from traditional talent-focused models to scalable, data-driven solutions.
The following case studies explore how these institutions are leveraging AWS expertise to improve clinical outcomes, reduce financial burden, and pioneer non-animal testing methods.
1. NHG Health uses chatbots to extend personalized patient care
NHG Health, in collaboration with national health technology agency Synapxe, wanted to address the complexity of hospital procedures with AI-powered patient engagement.
In just a few weeks, NHG Health developed an AI chatbot on the AWS platform that guided patients through complex step-by-step instructions.
By leveraging AWS serverless technology and Amazon Bedrock, NHG Health was able to quickly scale its application without having to burden itself with managing the underlying servers. This high-availability environment ensures that patients have access to the chatbot whenever they need it.
Beyond infrastructure, AWS also provided technical advice and credits, allowing the team to focus on improving AI accuracy and patient response.
Following the success of the pilot, NHG plans to extend the chatbot’s capabilities to other complex procedures, ultimately aiming to reduce long-term healthcare costs and improve patient outcomes across Singapore’s healthcare system.
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2. Ng Teng Phong General Hospital utilizes a population health management platform
Project ENTenna at Ng Teng Fong General Hospital (NTFGH) is Asia’s first population allergy database focused on allergic rhinitis and aims to move from episodic clinician-led care to continuous patient-led management.
The intervention produced important outcomes, including a 50% increase in medication adherence and an increase in patients successfully discharged from acute hospitals to community-based care.
This database was initially intended to address the lack of longitudinal data specific to Asia and the communication gaps inherent in traditional chronic disease management. Since then, it has emerged as a platform to integrate digital technologies such as generative and agentic AI into clinical practice to provide a more holistic model of care.
AWS provided the database foundation along with AI tools such as large-scale language models (LLMs) that power the platform’s communication tools. AWS also assisted NTFGH in preparing the materials required for regulatory approval and ensured that the platform met stringent public health standards.
Looking to the future, NTFGH envisions expanding its platform to manage other common chronic diseases such as diabetes, hypertension, and dementia.
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3. Singapore General Hospital brings hospital-grade care to your home through an AI-powered smartphone app
Singapore General Hospital (SGH) has developed BiliSG, a smartphone-based AI app designed to transform jaundice testing.
While traditional screening requires a tedious hospital visit, BiliSG allows parents to screen their baby from home using multiple smartphone photos.
The app is hosted on the AWS cloud, allowing thousands of patients to use the app at the same time, regardless of their location. AWS also provided essential technical support to integrate machine learning models developed by Synapxe’s data scientists.
This cloud-based approach has improved the patient experience by saving parents time, reducing the burden on crowded clinics, and addressing the nation’s economic challenges (the annual cost of jaundice testing is estimated to exceed $20 million).
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4. FRESH@NTU leverages machine learning and big data to advance non-animal testing
Moving away from traditional animal testing, Nanyang Technological University’s (NTU) Future Ready Food Safety Hub (FRESH) is leveraging AI and machine learning to more effectively analyze the vast amounts of big data generated from laboratory experiments.
AI has also enabled researchers to generate synthetic data to supplement limited experimental data to build more accurate predictive models for food safety.
FRESH@NTU leveraged high-performance computing resources in AWS and GPUs that far exceeded the capabilities of local labs, reducing computation time by at least 50%.
AWS provides an intuitively designed interface that allows NTU researchers to reduce programming time and focus on solving critical food safety problems instead of infrastructure management.
Beyond technology, AWS engineers and researchers provided guidance to NTU students and staff on algorithm selection and coding best practices. This collaboration culminated in the creation of a joint laboratory to advance more deep learning-based food safety research and enable the broader industry to adopt safer and more efficient testing methods.
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