ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations

MCML Authors

Anne-Laure Boulesteix

Prof. Dr.

Principal Investigator

Biometry in Molecular Medicine

Abstract

Modern large language models (LLMs) have reshaped the workflows of people across countless fields—and biostatistics is no exception. These models offer novel support in drafting study plans, generating software code, or writing reports. However, reliance on LLMs carries the risk of inaccuracies due to potential hallucinations that may produce fabricated “facts”, leading to erroneous statistical statements and conclusions. Such errors could compromise the high precision and transparency fundamental to our field. This tutorial aims to illustrate the impact of LLM-based applications on various contemporary biostatistical tasks. We will explore both the risks and opportunities presented by this new era of artificial intelligence. Our ultimate conclusion emphasizes that advanced applications should only be used in combination with sufficient background knowledge. Over time, consistently verifying LLM outputs may lead to an appropriately calibrated trust in these tools among users.

article DBB+25

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Statistics in Medicine

44.23-24. Oct. 2025.

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Authors

D. Dobler • H. Binder • A.-L. Boulesteix • J.-B. Igelmann • D. Köhler • U. Mansmann • M. Pauly • A. Scherag • M. Schmid • A. A. Tawil • S. Weber

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability