Artificial Intelligence (AI) is currently widely applied in the public health domain and has become a transformative force in this field. Through AI technologies such as machine learning, neural networks, and natural language processing, it has enabled epidemic prediction, optimization of health resource allocation, and enhancement of monitoring, screening, and diagnostic efficiency. These applications demonstrate significant implications for the future of public health. However, studies have identified three critical challenges hindering further AI adoption: data quality issues, implementation difficulties, and inherent limitations of AI technology. To address these challenges, effective recommendations are proposed, including innovating research methodologies through Practical Clinical Trials (PCT) and multidimensional evaluation matrices, promoting secure data governance via federated learning and edge computing, and establishing a global framework for algorithmic accountability and equitable technology transfer. As artificial intelligence evolves from an auxiliary tool, it is poised to become a core decision-support platform that will drive the transformation of public health from passive care to proactive preventionenabling early epidemic warnings, personalized chronic disease management and monitoring, as well as precision medical services.
Xiaowen Xu (Tue,) studied this question.