About the Journal
Applied AI (AAI) is an international, peer-reviewed, open access journal dedicated to the development, validation, deployment, and governance of artificial intelligence in real-world environments. The journal publishes original research, review articles, methods papers, case studies, perspectives, and short communications that advance the practical application of AI across industry, healthcare, engineering, business, public systems, and society.
The journal aims to provide a rigorous scholarly platform for work that connects computational innovation with operational implementation. It welcomes contributions that demonstrate methodological quality, reproducibility, technical depth, and clear domain relevance. Particular emphasis is placed on research that addresses applied machine learning, intelligent automation, AI systems integration, evaluation and benchmarking, responsible AI, and scalable deployment.
Applied AI seeks to bridge disciplinary boundaries and encourage collaboration among computer scientists, engineers, clinicians, data scientists, policy researchers, and domain specialists. By prioritizing practical relevance alongside scientific excellence, the journal aims to support the translation of AI research into measurable societal, industrial, and economic impact.
The journal operates under a full open access model, ensuring that all published content is freely and permanently accessible to readers worldwide.
Aims and Scope
Applied AI publishes high-quality contributions in all areas of applied artificial intelligence, including but not limited to:
- Applied machine learning and deep learning
- Generative AI and foundation models in practical settings
- Natural language processing and speech systems
- Computer vision and multimodal AI
- Industrial AI and intelligent automation
- Robotics and autonomous systems
- AI for healthcare, diagnostics, and clinical workflows
- AI for finance, logistics, operations, and supply chains
- Decision support systems and human-AI interaction
- Edge AI and embedded intelligent systems
- MLOps, deployment, monitoring, and lifecycle management
- AI evaluation, benchmarking, and validation
- Explainable, trustworthy, and responsible AI
- AI auditing, governance, and regulatory implementation
- Data-centric AI and intelligent systems integration
The journal encourages interdisciplinary, application-oriented, and solution-driven research with strong technical and practical contributions.