Speaker
Description
Preclinical experiments form the empirical foundation of translational medicine by assessing the feasibility, safety, and efficacy of new therapeutic approaches. Yet, unlike the highly regulated standards of clinical trials, preclinical research often exhibits substantial methodological heterogeneity, leading to concerns about reproducibility, bias, and the robustness of conclusions. These challenges are further intensified by the emerging use of artificial intelligence (AI). While AI has the potential to increase efficiency and support data analysis, uncontrolled or poorly understood applications can amplify existing weaknesses in study quality. In this paper, we discuss the critical importance of human judgment, statistical rigor, and transparent study design for ensuring reliable preclinical evidence. We examine key dimensions of analytical quality across study design, data generation, and evaluation, and consider how AI - particularly large language models (LLMs) and foundation models - can support, rather than undermine, methodological integrity. From a biometric perspective, safeguards for uncertainty quantification, error control, and interpretability are essential when integrating AI into preclinical research. Ultimately, methodological progress depends on a careful balance between human expertise, statistical inference, and computational tools. Illustrative examples highlight both opportunities and pitfalls at the interface of biostatistics, data science, and translational medicine, emphasizing that maintaining human oversight is crucial for generating reproducible and interpretable evidence.
21429409786