Our Takeaways from the 2024 ACE Value-Based Healthcare Conference and APAC MedTech Forum

In the last few weeks, I had the pleasure of attending the ACE Value-Based Healthcare Conference 2024 and the Asia Pacific MedTech Forum 2024, along with my colleagues, Jennifer Evans , Jian Yi Choy , Andrew Lim , and Jing Yi Wee . The use of AI in healthcare was the key theme across the conferences, and it was fascinating to hear about the various game-changing AI technologies that hold enormous potential to transform the future of healthcare. Here are our key takeaways.

AI holds transformative potential in enhancing healthcare, in terms of improving patient outcomes and the delivery of value-based healthcare, while addressing the challenges associated with ageing populations, rising healthcare costs, physician shortage and the burnout of healthcare professionals. In particular, predictive AI algorithms that can predict the risk of a disease, how a patient’s condition may progress, or the risk of treatment-related adverse events, could help inform clinical decision-making and aid Singapore’s move towards preventive healthcare. Notable examples include AI predicting conditions like diabetes from chest X-rays or systemic diseases from retinal images.

The promise of AI in healthcare is immense, but its success relies on a meticulous balance of effective human-AI collaboration, sound data governance, and ethical practices.

1.    AI is not intended to replace healthcare professionals; it should be recognised as a tool that augments human capabilities and enhances the work of healthcare professionals

Human resistance is the biggest impediment to realising the “value of scale” with AI. The need for human-centric AI was a prominent theme at the APAC MedTech Forum, emphasising the need for AI to be regarded as a tool to augment human intelligence, rather than replace it. By automating routine tasks and data entry, AI frees up valuable time from healthcare professionals to focus on more complex, higher-level tasks.

2.    Proper data governance is key to ensure the accuracy and reliability of AI-driven outcomes

As AI feeds off data, it is essential for the data to be clean, accurate and relevant to the local setting. For example, AI based on western data is not ready for global roll out; there need to be frameworks tailored to diverse cultural contexts, particularly in APAC regions. AI also potentially carries the biases of its creators. There’s therefore a need for human oversight to ensure proper data management and to keep a check on AI, potentially creating a new role for “AI auditors”.

3.    The ethical dilemma of AI needs to be addressed to gain public trust and achieve its full potential

Ethical and legal issues related to AI include data privacy and accountability – how can patients’ personal and sensitive data be protected, and who should be liable in case of a data breach or if mishaps occur due to AI? Additionally, ensuring AI-driven decisions are transparent, and having clear and evident data security measures are crucial for building trust among patients. This underscores the importance for guidelines to set the standard for how AI should be responsibly integrated into healthcare systems.

Having frameworks in place to address the above elements will help navigate the complexities of AI and translate its potential into tangible improvements in patient care. It is exciting to hear that the Singapore government is committed to investing in AI, and we are looking forward to witness more successful deployment of AI in healthcare!