How four Singapore public agencies accelerate AI with AWS

Singapore public agencies are turning to cloud-based artificial intelligence (AI) and machine learning to address complex national challenges. 

By providing access to high-performance computing, serverless architectures, and advanced generative AI tools, Amazon Web Services (AWS) acts as a force multiplier for these agencies.  

These partnerships have enabled the agencies to transition from ideation to minimum viable products (MVPs). The integration of AWS technologies is also moving public services from traditional, manpower-heavy models to scalable, data-driven solutions.  

The following case studies explore how these agencies are leveraging AWS expertise to improve clinical outcomes, reduce economic burdens, and pioneer animal-free testing methodologies. 

1. NHG Health scales personalised patient care with chatbots 

Collaborating with national healthtech agency Synapxe, NHG Health wanted to tackle the complexities of hospital procedures with AI-driven patient engagement.  

Within just a few weeks, NHG Health developed an AI chatbot on the AWS platform to guide patients through complex, step-by-step instructions. 

By utilising AWS serverless technology and Amazon Bedrock, NHG Health was able to scale the application rapidly without the burden of managing the underlying servers. This high-availability environment ensures that the chatbot is accessible whenever patients need it.  

Beyond infrastructure, AWS also provided technical advisory and credits, enabling the team to focus on refining the AI's accuracy and patient resonance. 

Following the success of the pilot, NHG plans to expand the chatbot’s capabilities to other complex procedures, ultimately aiming to reduce long-term healthcare costs and improve patient outcomes across the Singapore healthcare system. 

You can watch the full case study video here >> 

2. Ng Teng Fong General Hospital leverages population health management platform 

Ng Teng Fong General Hospital (NTFGH)’s Project ENTenna, which is Asia’s first population allergy database focused on allergic rhinitis, was aimed to shift from episodic, clinician-led care to continuous, patient-empowered management. 

This intervention has yielded significant results, including a 50 per cent increase in medication adherence and a rise in patients being successfully discharged from acute hospitals to community-based care. 

The database was initially intended to address the lack of longitudinal Asian-specific data and the communication gaps inherent in traditional chronic disease management. It has since emerged into a platform that integrates digital tech, including generative and agentic AI, into clinical practice to provide a more holistic model of care. 

AWS provided the database foundation alongside the AI tools, including large language models (LLMs) that powered the platform’s communication tools. AWS also assisted NTFGH in preparing the necessary materials for regulatory approval, ensuring the platform met stringent public health standards.  

Looking forward, NTFGH envisions expanding the platform to manage other prevalent chronic conditions such as diabetes, hypertension, and dementia. 

You can watch the full case study video here >> 

3. Singapore General Hospital’s enables hospital-level care at home through AI-powered smartphone app 

Singapore General Hospital (SGH) has developed BiliSG, a smartphone-based AI app designed to transform jaundice screening.  

While traditional screening involves cumbersome hospital visits, BiliSG allows parents to screen their babies from home via multiple smartphone photographs. 

The app was hosted on the AWS cloud, which allowed thousands of patients to use the app simultaneously regardless of their location. AWS also provided technical support which was vital in integrating the machine learning model developed by data scientists at Synapxe. 

This cloud-based approach has improved the patient experience by saving time for parents, reducing the burden on crowded clinics, and addressing an economic challenge for the country (with the annual cost of jaundice screening estimated to exceed $20 million). 

You can watch the full case study video here >> 

4. FRESH@NTU taps into machine learning and big data to drive animal-free testing 

To move away from traditional animal testing, the Future Ready Food Safety Hub (FRESH) at Nanyang Technological University (NTU) is utilising AI and machine learning to analyse vast big data generated from lab experiments more effectively. 

AI has also allowed researchers to generate synthetic data, which complements limited experimental data to build more accurate predictive models for food safety. 

FRESH@NTU has leveraged AWS’s high-performance computing resources and GPUs, which far exceeded local lab capacities, to reduce computation time by at least 50 per cent. 

An intuitively designed interface provided by AWS has also allowed NTU’s researchers to shorten their programming time, enabling them to focus on solving critical food safety problems rather than infrastructure management. 

Beyond technology, AWS engineers and researchers provided mentorship to NTU students and staff on algorithm selection and coding best practices. This collaboration has culminated in the establishment of a joint lab to drive more deep learning-based food safety research and to ensure that the broader industry can adopt safer, more efficient testing methodologies. 

You can watch the full case study video here >>