JMIR Publications

ABSTRACT

In the ongoing Artificial Intelligence (AI) boom—now supercharged by Large Language Models and generative tools—the rapid integration of AI into healthcare and public health has created an urgent need for professionals who combine deep domain expertise (clinical, epidemiological, ethical, and contextual expertise in health and public health) with AI fluency: the ability to understand, critically evaluate, use, and oversee AI systems effectively. Without domain guidance, AI risks producing biased, unsafe, or contextually irrelevant outputs that compromise patient safety and disparity. Conversely, domain experts lacking AI fluency may underuse or misuse these technologies, missing opportunities to improve care quality, operational efficiency, and population health. A foundational understanding of the modern AI System Stack—comprising Energy, Accelerators, Infrastructure, Models, and Applications—empowers health professionals to make informed decisions on adoption, evaluation, governance, and ethical use without needing to code or build models themselves. This stack framework highlights critical interdependencies and healthcare-specific challenges, including regulatory compliance, data privacy, sustainability, and resource constraints, particularly in low- and middle-income settings. This paper argues that cultivating domain-AI bilingualism is essential for safe, effective, and equitable AI integration globally. It outlines the five-layer AI System Stack applied to healthcare, key reasons for hybrid expertise, and a practical four-step pathway: Awakening (recognizing AI’s transformative role), Intrepidity (courageously engaging foundational AI concepts), Bilingualism (translating between clinical needs and AI capabilities), and Visionary Leadership (driving organizational and policy readiness). It outlines major barriers, targeted strategies and recommendations for educators, institutions, and policymakers. By bridging domain knowledge with AI fluency, health professionals can direct—rather than be directed by—AI technologies, including emerging agentic systems, toward more precise, sustainable, and human-centered outcomes while mitigating risks. This hybrid capability represents a non-negotiable competency for health workforce for thriving in the post-2026 AI era.