Journal of Artificial Intelligence and Digital Health
Big Data in Medicine
Big Data in Medicine involves collecting, analyzing, and interpreting extremely large and complex healthcare datasets to improve clinical decision-making and patient outcomes. These datasets include electronic health records, imaging files, genomic data, wearable sensor outputs, and population health statistics. By applying AI and advanced analytics, big data helps identify disease patterns, predict risks, and personalize treatment strategies. It supports public health surveillance, clinical research, and operational efficiency in hospitals. Big data also enables early detection of outbreaks and optimizes resource allocation. Overall, it empowers healthcare systems to become more proactive, precise, and evidence-driven.
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DOAJ
CrossRef
PubMed
MEDLINE
ResearchBib
OAJI
Sindexs
EBSCO A-Z / Host
OCLC - WorldCat
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Journal Highlights
Artificial Intelligence in Healthcare
Machine Learning and Deep Learning
Medical Imaging and Diagnostics
Predictive Analytics
Digital Health Technologies
Telemedicine and mHealth
Wearable Devices and Remote Monitoring
Health Informatics and EHR
Big Data in Medicine
Robotics and Automation
AI in Drug Discovery
Healthcare Cybersecurity
Smart Hospital Systems
Clinical Decision Support Systems
Natural Language Processing
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