Large Language Models for Global Health Clinics: Opportunities and Challenges

Abstract

Large language models (LLMs) have emerged as a new wave of artificial intelligence, and their applications could emerge as a pivotal resource capable of reshaping health care communication, research, and informed decision-making processes. These models offer unprecedented potential to swiftly disseminate critical health information and transcend linguistic barriers. However, their integration into health care systems presents formidable challenges, including inherent biases in training data, privacy vulnerabilities, and disparities in digital literacy. Despite these obstacles, LLMs possess unparalleled analytic prowess to inform evidence-based health care policies and clinical practices. Addressing these challenges necessitates the formulation of robust ethical frameworks, bias mitigation strategies, and educational initiatives to ensure equitable access to health care resources globally. By navigating these complexities with meticulous attention and foresight, LLMs stand poised to catalyze substantial advancements in global health outcomes, promoting health equity and improving population health worldwide.