ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations
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.
Authors
- André Scherag (ORCID: https://orcid.org/0000-0002-9406-4704)
- Markus Pauly (ORCID: https://orcid.org/0000-0002-0976-7190)
- Harald Binder (ORCID: https://orcid.org/0000-0002-5666-8662)
- Anne‐Laure Boulesteix (ORCID: https://orcid.org/0000-0002-2729-0947)
- Matthias Schmid (ORCID: https://orcid.org/0000-0002-0788-0317)
- David Köhler (ORCID: https://orcid.org/0009-0006-0027-4046)
- Dennis Dobler (ORCID: https://orcid.org/0000-0002-9040-0854)
- Ulrich Mansmann (ORCID: https://orcid.org/0000-0002-9955-8906)
- Susanne Weber (ORCID: https://orcid.org/0000-0002-4418-3836)
- Amani Al Tawil
- Jan‐Bernd Igelmann
Institutions
- University of Freiburg (DE)
- TU Dortmund University (DE)
- University Hospital Bonn (DE)
- University Medical Center Freiburg (DE)
- Zimmer Biomet (Germany) (DE)
- Alliance Data (United States) (US)
- Jena University Hospital (DE)
- Munich Center for Machine Learning
- Friedrich Schiller University Jena (DE)
- Ludwig-Maximilians-Universität München (DE)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Statistics in Medicine
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1002/sim.70263
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.31
Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Ludwig-Maximilians-Universität München
- Universitätsallianz Ruhr
- Technische Universität Dortmund