Artificial intelligence foundation models in healthcare: A Malaysian perspective

Area of Science:

• Artificial Intelligence
• Deep Learning
• Precision Health

Background:

• Foundation models represent a significant advancement in AI, trained on vast datasets for diverse applications.
• Their adoption in healthcare is accelerating due to digitalization, enabling holistic patient understanding.
• These models integrate medical imaging, clinical notes, and genomic data for personalized interventions.

Purpose of the Study:

• To explore the role of foundation models in advancing precision health insights.
• To propose a perspective on integrating foundation models into the Malaysian healthcare system.
• To identify governance, ethical, regulatory, and policy challenges for implementation in Malaysia.

Main Methods:

• This editorial reviews current advancements in foundation models.
• It analyzes the potential impact and challenges of their application in healthcare.
• It discusses the specific context of the Malaysian healthcare landscape.

Main Results:

• Foundation models are key drivers of insights in precision health.
• Integration into Malaysia's healthcare system offers significant potential for personalized care.
• Implementation faces substantial governance, ethical, regulatory, and policy hurdles.

Conclusions:

• Foundation models are transforming healthcare by enabling data integration and personalized medicine.
• Strategic integration into Malaysia's healthcare system is feasible but requires careful consideration of challenges.
• Addressing ethical and regulatory frameworks is crucial for successful adoption.