Integrative Innovation of Healthcare Informatics and AI: Revolutionizing the Healthcare Landscape

Abstract

The integration of Healthcare Informatics and Artificial Intelligence (AI) has emerged as a transformative force in modern healthcare, addressing critical challenges such as inefficient data management, limited real-time patient monitoring, and fragmented medical knowledge. This paper systematically explores three core domains of this integration: AI embedding in Health Information Systems (HIS) and Laboratory Information Systems (LIS), AI-driven applications in Mobile Health (mHealth) and wearable devices, and the construction and application of medical knowledge graphs. Through case studies—including AI-enhanced HIS for clinical workflow optimization, wearable-based real-time monitoring of chronic disease patients, and knowledge graph-assisted interpretation of rare disease mechanisms—the paper demonstrates the practical value of these innovations in improving diagnostic accuracy, enhancing patient self-management, and accelerating medical research. Additionally, the study identifies key challenges, such as data security risks, system compatibility issues, and ethical dilemmas, and proposes targeted solutions, including end-to-end encryption technologies and cross-regional regulatory frameworks. Finally, future trends, such as multi-modal data fusion and edge computing in AI-healthcare integration, are discussed to provide insights for researchers and healthcare practitioners. This work contributes to the advancement of evidence-based AI applications in healthcare informatics, aiming to drive more efficient, patient-centered, and sustainable healthcare systems.