ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations
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2025
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| author | Dennis Dobler Harald Binder Anne‐Laure Boulesteix Jan‐Bernd Igelmann David Köhler Ulrich Mansmann Markus Pauly André Scherag Matthias Schmid Amani Al Tawil Susanne Weber |
| author_facet | Dennis Dobler Harald Binder Anne‐Laure Boulesteix Jan‐Bernd Igelmann David Köhler Ulrich Mansmann Markus Pauly André Scherag Matthias Schmid Amani Al Tawil Susanne Weber Dennis Dobler Harald Binder Anne‐Laure Boulesteix Jan‐Bernd Igelmann David Köhler Ulrich Mansmann Markus Pauly André Scherag Matthias Schmid Amani Al Tawil Susanne Weber |
| collection | Wiley Open Access |
| contents | ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations Dennis Dobler Harald Binder Anne‐Laure Boulesteix Jan‐Bernd Igelmann David Köhler Ulrich Mansmann Markus Pauly André Scherag Matthias Schmid Amani Al Tawil Susanne Weber Statistics in 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. 10.1002/sim.70263 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/sim.70263 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70263 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations Dennis Dobler Harald Binder Anne‐Laure Boulesteix Jan‐Bernd Igelmann David Köhler Ulrich Mansmann Markus Pauly André Scherag Matthias Schmid Amani Al Tawil Susanne Weber Statistics in Medicine ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations Dennis Dobler Harald Binder Anne‐Laure Boulesteix Jan‐Bernd Igelmann David Köhler Ulrich Mansmann Markus Pauly André Scherag Matthias Schmid Amani Al Tawil Susanne Weber Statistics in 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. 10.1002/sim.70263 http://creativecommons.org/licenses/by/4.0/ |
| title | ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70263 |