Challenges and Transformations in Biomedical Statistics Research Driven by Artificial Intelligence

ABSTRACT

With the rapid advancement of artificial intelligence (AI) technology, the field of biomedical statistics has embraced novel solutions in areas such as data processing, variable control, and model formulation. This paper explores how AI technology is driving a transformation in the research design framework and conceptual paradigms of biomedical statistics. The focus is, particularly, on the challenges faced by AI‐driven causal inference research, including issues with the misinterpretation of associations, data noise interference, and shortages of methods. To address these challenges, this paper proposes a solution strategy centered on transparent algorithm design, the integration of heterogeneous models, and third‐party validation mechanisms. These strategies aim to provide a methodological foundation for bridging the gap between AI‐driven statistical associations and biological mechanisms.

This article is categorized under:

Algorithmic Development > Statistics