Details
Author
CHEAB Sambath, MD, MHPE, PhD student (STS)
Category
This article explores the integration of generative artificial intelligence (GenAI) in healthcare research, balancing its efficiency and methodological benefits with potential risks to research integrity, data privacy, and ethical responsibility. Focusing on the context of healthcare professionals in low- and middle-income countries like Cambodia, the author proposes a Model-Mode-Task-Verification paradigm to establish safe, structured workflows for literature exploration, study design, data analysis, and academic writing. Ultimately, the article emphasizes that GenAI must serve strictly as a disciplined, transparently disclosed assistant, leaving final accountability, epistemic authority, and human judgment entirely in the hands of the researchers.
This paper explores the macroeconomic “AI displacement trap,” arguing that simultaneous global automation destroys the primary engine of economic demand by eliminating human wage income.…
This examines how small states can navigate digital sovereignty within an AI landscape dominated by major powers like the United States and China. Rather than…
This article examines how Buddhist ethical principles can inform the development and governance of artificial intelligence. It argues that while AI is transforming sectors such…
This reflective essay explores how technological change reshapes the meaning of sovereignty, particularly for small nations navigating the AI era. Drawing on a multidisciplinary career…
Effective use of generative AI (GenAI) requires transitioning from a “single-tool mindset” to a deliberate system that aligns task purposes with specific model capabilities, operational…
This paper critiques dominant AI ethics frameworks that focus on machines rather than the social and cultural contexts in which they operate. It argues that…