The aim of this review is to explore and critically assess the current applications of artificial intelligence (AI) in medical biostatistical research, encompassing both theoretical perspectives and real-world applications. AI techniques, such as deep learning, support vector machines, decision trees or clustering, expands upon traditional biostatistics to enable systematic processing of complex and high-dimensional unstructured biomedical datasets. AI has revolutionized the research methodologies employed in traditional medical biostatistics, providing new tools for data analysis, predictive modeling, and integration of heterogeneous data sources. AI promises to add value in many medical domains - disease risk prediction, clinical trial analysis, omics integration, and precision medicine, to name a few. Combined with traditional statistical techniques, AI provides more complex and comprehensive insights into the structure of medical information, having the potential to respond optimally to the expanding medical needs for swift, precise, and personalized treatments.
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