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Aims Health Informatics and AI (HIAI) is a peer-reviewed, interdisciplinary journal committed to advancing the integration of informatics principles, data science methodologies, and artificial intelligence (AI) technologies in healthcare and public health contexts. As digital transformation reshapes clinical workflows, patient care delivery, population health management, and medical research—addressing critical needs such as data-driven decision-making, personalized medicine, efficient healthcare operations, and equitable access to health services—this journal serves as a platform for cutting-edge research on the intersection of health informatics innovations, AI advancements, and real-world health challenges. It focuses on bridging technical expertise with clinical/population health priorities, promoting evidence-based development and deployment of health informatics and AI tools, and fostering collaboration among researchers, clinicians, technologists, and policymakers to create scalable, ethical, and patient-centered solutions that improve health outcomes globally.
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Scopes
The journal covers interdisciplinary research at the intersection of healthcare, informatics, and AI, including but not limited to the following core areas:
AI for Healthcare Data Processing and Analysis:Technologies for standardization and structuring of healthcare data, AI-driven healthcare data mining, Privacy protection and security technologies for healthcare big data
Applications of AI in Clinical Diagnosis, Treatment, and Decision Support:Auxiliary diagnostic systems, Clinical decision support tools, Optimization of clinical workflows
Integrative Innovation of Healthcare Informatics and AI: AI integration in health information systems, AI applications in mobile health (mHealth) and wearable devices, Construction and application of medical knowledge graphs
AI Empowerment for Healthcare Systems and Public Health: Optimization of healthcare resource allocation, Public health monitoring and emergency response, Healthcare quality assessment and improvement
Methodologies and Case Studies in Interdisciplinary Fields: Development of new AI algorithms/models for healthcare, Innovation in interdisciplinary research methods, Real-world case studies
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