Will generative AI replace biostatisticians? Opportunities, challenges, and professional responsibility in the era of large language models.: Generative AI and biostatisticians

Alessandro Marcon; Valentina Panetta; Giuseppe Maglietta; Lorenza Scotti; Vittorio Simeon; Giovanni Veronesi

doi:10.54103/2282-0930/31293

Authors

Alessandro Marcon University of Verona https://orcid.org/0000-0002-2778-658X
Valentina Panetta L'altrastatistica s.r.l., Rome, Italy https://orcid.org/0000-0001-6058-5045
Giuseppe Maglietta University Hospitals Parma Medical Center https://orcid.org/0000-0003-4497-1872
Lorenza Scotti Università degli Studi del Piemonte Orientale “Amedeo Avogadro” https://orcid.org/0000-0002-3861-9058
Vittorio Simeon University of Campania "Luigi Vanvitelli" https://orcid.org/0000-0003-2387-4125
Giovanni Veronesi University of Insubria https://orcid.org/0000-0002-4119-6615

DOI:

https://doi.org/10.54103/2282-0930/31293

Keywords:

Accountability, Biostatistics, Generative Artificial Intelligence, Natural Language Processing, Reproducibility of Results

Abstract

The rapid diffusion of large language models (LLMs) is reshaping many aspects of biomedical research, prompting reflection on the evolving role of biostatisticians in a changing landscape. This commentary addresses this transformation from two complementary perspectives. First, we examine how LLMs can assist biostatisticians in professional practice, highlighting their potential to enhance efficiency, support complex analytical reasoning, and facilitate communication and training. Second, we discuss the limitations and risks associated with LLM use, including challenges to reproducibility, susceptibility to bias, data protection and regulatory constraints, and limited accountability. Finally, we outline a vision for the role of scientific societies in actively guiding this transition. By promoting methodological competence, ethical awareness, and professional identity, they can help ensure that generative AI becomes an instrument of responsible innovation rather than a source of methodological bias and epistemic uncertainty.

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Will generative AI replace biostatisticians? Opportunities, challenges, and professional responsibility in the era of large language models.

Generative AI and biostatisticians

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