Statistical issues in general (Being human in preclinical research)
August 26, 2026 In their article, Lang et al (2026) discuss the issues of assessing quality in preclinical research statistically in the age of the emerging use of artificial intelligence (AI). They aimed to show how large language models (LLMs) and more generally foundation models can enhance rather than undermine the quality of preclinical research while highlighting important role of human expertise, statistical rigor, and study design. They then mentioned two scenarios assuming that statistical support is currently optional: 1)When experts are present, AI should augment rather than replace their role and 2)When experts are absent, the introduction of autonomous AI by nonexperts poses additional risks beyond those already present in current practice. They had described these stages as a hierarchy like a pyramid. For example, if the lower level is compromised in terms of quality than the upper levels will not be able to fix this. They suggested that the risk inherent in this dependency can be addressed by the pharmaceutical industry's wellestablished concept of Quality by Design, which is a framework that promotes proactive planning and control to ensure that quality is built into each step of a process rather than tested into the output afterwards or addressed reactively at later stages (Yang et al. 2025; U.S. Food and Drug Administration, 2025). Some of the well-known risks of autonomous AI tools include hallucinations, incomplete suggestions, and overconfidence while the benefits which include efficacy, as time-consuming tasks can be automated. In terms of data generation, they state that autonomous AI can assist in creating drafts for statistical plans, particularly regarding sample size planning and randomization procedures and also that any document creation can be sped up immensely. However they do recommend that in order to ensure quality, supervision by a statistics expert is mandatory to assess whether all relevant aspects of the experimental design are covered and phrased correctly and also to ensure that the sample size and randomization process are appropriate. Also, their suggestion for data analysis is that many programming and coding tasks can be effectively managed by autonomous AI support. However, once again, they recommend that accountability of results should be part of an expert role and not delegated to a nonexpert who only understand running the AI to create code but doesn’t understand how to create the proper checks and oversight.