ORCID: https://orcid.org/0000-0002-9040-0854; Binder, Harald; Boulesteix, Anne‐Laure ORCID: https://orcid.org/0000-0002-2729-0947; Igelmann, Jan‐Bernd ORCID: https://orcid.org/0009-0001-6994-4945; Köhler, David; Mansmann, Ulrich ORCID: https://orcid.org/0000-0002-9955-8906; Pauly, Markus; Scherag, André ORCID: https://orcid.org/0000-0002-9406-4704; Schmid, Matthias; Al Tawil, Amani ORCID: https://orcid.org/0000-0002-4093-5447 und Weber, Susanne
(2025):
ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations.
In: Statistics in Medicine, Vol. 44, e70263
[PDF, 3MB]
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.
| Item Type: | Journal article |
|---|---|
| Keywords: | causal analysis; diagnostic accuracy; generative AI; individual‐level surrogacy; large language model; latent class analysis; meta‐analysis; sample sizes planning; simulation study; translation programming languages |
| Faculties: | Medicine > Institute for Medical Information Processing, Biometry and Epidemiology |
| Subjects: | 600 Technology > 610 Medicine and health |
| URN: | urn:nbn:de:bvb:19-epub-131119-7 |
| ISSN: | 0277-6715 |
| Language: | English |
| Item ID: | 131119 |
| Date Deposited: | 16. Jan 2026 07:43 |
| Last Modified: | 16. Jan 2026 07:43 |
| DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 413270747 |
| DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 499552394 |
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