Seed Funding

Large Language Models (LLMs) have significantly advanced our ability to analyze and interpret text within health-related domains, offering transformative potential in applications such as patient-facing chatbots, clinical decision support systems, and public health communications. Despite these advancements, current LLMs face critical challenges, including issues with accuracy, hallucination, and the potential to propagate misinformation. These limitations pose significant risks when applied to sensitive health contexts.This research question addresses these challenges by exploring and deploying methods to enhance the accuracy, reliability, and overall trustworthiness of LLMs in health applications.

The integration of AI and data-driven approaches into public health policy holds transformative potential for improving population health outcomes. However, the application of AI in this context requires careful consideration to ensure that policies are evidence-based, equitable, and responsive to the dynamic needs of communities. This topic explores innovative ways to leverage AI and data in the development, implementation, and evaluation of public health policies. Areas of interest include predictive modeling for emerging health threats, optimizing resource allocation in health interventions, and ensuring that AI-driven policies are inclusive and minimize health disparities.

The widespread adoption of wearable devices offers an unparalleled opportunity to revolutionize public health and health care. By continuously collecting real-time health data, these devices provide insights into both individual and population-level health trends. This topic investigates how AI, in combination with wearable technology, can advance public health initiatives and enhance healthcare delivery. Key areas of interest include leveraging AI to analyze wearable-generated data for early disease detection, developing personalized health interventions, and enabling real-time monitoring of critical public health metrics.

This question seeks to understand the effects of digital technologies, such as smartphones and social media, on human cognitive processes and behaviors. Researchers should explore how these technologies influence attention spans, memory, decision-making, and social interactions. Additionally, understanding the long-term effects of digital technology use on children and adolescents is crucial. This research could inform the design of technology that supports rather than hinders cognitive development and mental well-being.

As AI and automation become increasingly integrated into workplaces, it’s essential to explore the ethical implications for workers. Key questions include: How do these technologies affect job security, privacy, and the nature of work? What are the implications for human agency and autonomy when decision-making is increasingly driven by algorithms? This research should also consider how to ensure that AI systems are designed and deployed in ways that are fair, transparent, and inclusive, addressing concerns of bias and inequality in their application.

This question focuses on the positive potential of digital technologies to improve human lives and society. Research should investigate how digital tools can be used to promote physical and mental well-being, foster community engagement, and address social challenges such as inequality and access to education. Understanding how to design and implement technology that enhances well-being while minimizing potential harms is critical. This research can guide the development of technologies that are not only innovative but also socially responsible and beneficial to humanity as a whole.