Generative AI in public healthcare: Key takeaways from Synapxe’s Generative AI Week 2024

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Emerging technologies are transforming modern healthcare delivery through AI innnovation. 


As technology evolves, healthcare professionals and tech leaders constantly explore new advancements. In recent years, Generative AI (GenAI) has emerged as a potential game changer in healthcare.

But what exactly is Generative AI, and how can it be harnessed to benefit the healthcare sector?

Unlike traditional artificial intelligence (AI) that focuses on analysing and classifying existing data, Generative AI takes a step further. It is a field of AI capable of creating new written, visual, and auditory content. Generative AI is not only built on large language models (LLMs), but also on other sophisticated machine learning models. Examples of Generative AI in action would be OpenAI’s ChatGPT, and Google’s Gemini.

To delve deeper, Synapxe kicked off 2024 with its first Generative AI Week 2024 for all professionals from Singapore’s public healthcare institution and agencies. This week-long virtual event welcomed leading experts from the healthcare and technology domains to share their insights and explore the potential applications of Generative AI in healthcare.

Generative AI event by Synapxe: 11 Key Takeaways

Don’t just take it from us. Let’s take a closer look at what our esteemed industry speakers had to share about Generative AI. Here are 11 key takeaways that can change your mind about Generative AI: 


1. THE EVOLUTION OF DATA AND AI IN HEALTHCARE by Sandeep Basu, Director, Data & AI, Microsoft ASEAN:

Generative AI has the potential to reinvent the healthcare industry across areas such as patient engagement, improving clinical and operational outcomes. It must be used in a responsible fashion, and the Microsoft Responsible AI framework can be used as a guide to build AI systems.


2. GENERATIVE AI – FRIEND OR FOE by A/Prof Ngiam Kee Yuan, Group Chief Technology Officer, National University Health System:

There is great potential to apply Generative AI tools in many applications, however, users need be educated on how to best exploit the use of these tools and familiarise themselves with the benefits and risks of using Generative AI.


3. HOW GENERATIVE AI CAN HELP IN PRODUCTIVITY TRANSFORMATION by John Rozier, R&D Lead, Clinical Applications & AI, Epic:

Generative AI can be applied into healthcare user workflows to help speed up and reduce administrative work, such as drafting and summarising. While the capabilities of Generative AI continue to evolve rapidly, it is important to understand what is already possible today, and how this technology should be used responsibly.

4. BENCHMARKING LLMS FOR CLINICAL TEXT CLASSIFICATION by Dr Vicente Sancenon, Lead Specialist, Data aNalytics & Ai (DNA), Synapxe and Huang Yiting, Specialist, DNA, Synapxe:

The potential benefits of implementing LLMs must be rigorously assessed against potential costs on a case-by-case basis, and one consideration would be the availability of training data.


5. INTRODUCING SYNAPXE’S VERY OWN AI COLLABORATORS AND WHAT THEY CAN DO by Alvin Yuen, Senior Specialist, DNA (Data Science & AI), Synapxe and Naveen Tiwari, Manager, DNA (Services Planning), Synapxe:

Effective prompt engineering, search retrieval, and fine-tuning are essential for optimising the performance of language models in healthcare applications.

6. A PRACTICAL GUIDE TO EMBARKING ON YOUR GENERATIVE AI JOURNEY by Dr Kelvin Li, Ophthalmology Clinical Lead, Innovation and Digitalisation, National Healthcare Group (Tan Tock Seng Hospital) and Dr Daniel Lim, Gastroenterology Senior Resident, SingHealth (Singapore General Hospital):

It is important to evaluate LLMs regularly to ensure that they are not only generating high-quality text, but also aligned to prevailing standards of humans performing the same task.

7. ENGAGEMENT AND EMPOWERMENT WITH GENERATIVE AI TOOLS & USE CASES FOR HEALTHCARE by Richard Goh, Head of Singapore Public Sector Business Development & Cloud Innovation Center, Amazon Web Services (AWS) and Eugene Ng, Solution Architect, Worldwide Public Sector, AWS:

There are different techniques like Retrieval Augmented Generation (a technique used in AI to improve accuracy and reliability of generated text) to improve the output and outcomes of Generative AI applications. Generative AI applications, such as in drug discovery and clinical trials, can be used from the initial stages of development – like data collection and throughout the entire process, including performance optimisation and monitoring.

8. DELIVERING SAFE AND RESPONSIBLE AI by Sutowo Wong, Director, Health Analytics Division at Ministry of Health (MOH):

AI projects can be grounded by putting people at the center of the AI universe. We can strike a balance in ensuring safe growth of AI by taking a risk-based approach to prevent an overly conservative interpretation of governance framework.

9. BALANCING AI-INNOVATION AND ETHICS IN HEALTHCARE by Chandan Bhattacharjee, Account Technical Leader, IBM:

Striking a balance between Generative AI innovation and ethics is important. Ethics by Design is essential and it is a shared responsibility.

10. TRANFORMING HEALTHCARE WITH LLM/GENERATIVE AI TECHNOLOGY by Dambo Ren, Regional Head, Cloud AI Customer Engineering (SEA), Google Cloud:

Generative AI can enhance healthcare by speeding up data-intensive processes, automating manual processes, streamlining repetitive tasks, and accelerating the review of disparate data sources. Healthcare is one of the industries that is most impacted by Generative AI, with potential impact that includes 14-25% productivity gains.

11. THE FUTURE OUTLOOK: GENERATIVE AI’S TRANSFORMATIVE JOURNEY by Ramine Tinati, Managing Director, Applied Intelligence, Data Science & Machine Learning Practice in SEA, Accenture:

Great technology is pervasive, and AI embedded in such technologies will make them more intelligent. Trust is critical, especially so with the use of AI. Hence, organisations will need to focus on ensuring that AI is designed and used responsibly.

How Generative AI could change public healthcare in Singapore

Adopting Generative AI in a safe and secure manner can aid healthcare professionals in optimising workflows, enhancing operational efficiency, elevating patient care experiences and more. In 2023, Singapore launched the Singapore National AI Strategy 2.0 (NAIS 2.0) with three major shifts from the initial 2019 National AI Strategy. With its strong focus on technology adoption and commitment to driving global innovation, Singapore is actively exploring the potential of Generative AI in healthcare.

In collaboration with Microsoft, Synapxe is developing a SecureGPT tool that can assist public healthcare professionals with tasks like finding relevant information from medical notes and summarising patient data. The tool aims to extract key insights with accuracy, allowing healthcare professionals to quickly access crucial information among extensive documentation. Concise summaries can be produced to highlight important details – in return, healthcare professionals will be able to make better-informed decisions and dedicate more time to patients and their needs. Many institutions in Singapore are also exploring the use of Generative AI for drug discovery, detection of anomalies in medical imaging, and more to improve efficiency and accuracy.

It’s important to note that Generative AI in healthcare is still in its early stages of development and with any emerging technology, it’s crucial to avoid potential biases in training data and ensure ethical and secure development.

What excites you most about the potential of Generative AI in HealthTech? For more information on health AI projects:


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