Methods Mol Biol. 2025;2952:411-428. doi: 10.1007/978-1-0716-4690-8_22.
ABSTRACT
The integration of Artificial Intelligence (AI) with conventional biostatistics represents a pivotal advancement in healthcare research. The synergistic relationship between traditional biostatistical approaches and modern AI techniques highlights their complementary roles in advancing medical research and clinical practice. While conventional biostatistics provides robust frameworks for hypothesis testing, parameter estimation, and statistical inference, AI offers enhanced capabilities in pattern recognition, predictive modeling, and the analysis of complex, high-dimensional datasets. This chapter explores the fundamental concepts and the complementary strengths of these two paradigms, discussing their foundational principles, practical applications, critical challenges, and ethical considerations in clinical practice. Furthermore, we present emerging trends and future directions, focusing on explainable AI, hybrid modeling approaches, real-time data integration, and advances in precision medicine. The comprehensive analysis illuminates the convergence of AI and biostatistics is revolutionizing healthcare research while maintaining scientific rigor and statistical validity, ultimately paving the way for more sophisticated, efficient, and personalized healthcare solutions.
PMID:40553345 | DOI:10.1007/978-1-0716-4690-8_22
