Artificial Intelligence in Health is an online open-access, multidisciplinary journal dedicated to publishing high-quality peer-reviewed research in all areas of Artificial Intelligence in health and medicine science. By publishing high-quality research papers, reviews, and case studies, the journal seeks to contribute to the scientific community's understanding of the potential, challenges, and impact of AI and its applications on health delivery, patient outcomes, and population health.

Topics covered include, but are not limited to:

• AI-based medical diagnosis and prognosis covers research on the development and application of AI techniques, such as machine learning, deep learning, and natural language processing, for accurate medical diagnosis and prognosis. It includes studies on Bio's multi-omics analysis, image analysis, pattern recognition, clinical data mining, and predictive modeling to aid in the identification, classification, and prediction of diseases.
• AI clinical decision support systems encompasses research on the design, development, and evaluation of AI-driven clinical decision support systems. Topics include algorithms for treatment recommendation, patient risk stratification, personalized medicine, and decision-making tools that integrate patient data, clinical guidelines, and evidence-based knowledge.
• AI-driven drug discovery and development focuses on AI techniques applied to accelerate drug discovery, including virtual screening, molecular modeling, and predictive analytics. It also covers the use of AI in drug repurposing, toxicity prediction, and pharmacovigilance to enhance the efficiency and safety of pharmaceutical development processes.
• AI-enabled healthcare operations and management, and the research and application in telemedicine involves research on the utilization of AI to optimize healthcare operations, resource allocation, and workflow management. Topics include patient flow optimization, bed management, resource allocation for healthcare facilities, scheduling and routing algorithms, and predictive analytics for resource demand forecasting.
• AI-assisted electronic health records and clinical informatics encompasses research on AI techniques applied to improve the quality, interoperability, and usability of electronic health records (EHRs). This topic includes studies on data integration, natural language processing for EHR analysis, clinical decision support embedded within EHR systems, and privacy and security considerations in AI-driven healthcare informatics.
• AI-based research and application of wearable devices for diagnosis and treatment revolves around the development of equipment with intelligent medical symptom detection, intelligent diagnosis and treatment with the aid of AI technology, as well as the development of applications such as remote health management, remote room inspection, remote examination, intelligent examination and diagnosis.
• Social implications of AI in health addresses the social implications associated with the integration of AI in healthcare. Topics include privacy, data security, algorithmic bias, explainability and interpretability of AI models, regulatory considerations, and frameworks for AI-driven healthcare decision-making.