ArticleStatistics in medicine2025
ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations.
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Design, simulate, refine: simulation-guided clinical trials for accelerated drug development.Nature reviews. Drug discovery · 2026Review
- Ten simple rules for effective use of generative AI for code development in environmental science.PLoS computational biology · 2026Article
- Statistical Competence and Editorial Policies in Biomedical Publications.Journal of Korean medical science · 2026Review
- [Evidence generation and methodological consultation by the project "EVAluation research based on data from routine clinical care 4 the Medical Informatics Initiative" (EVA4MII)].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2026Review
- Evaluating the Accuracy of Large Language Models in Selecting Appropriate Statistical Tests for Healthcare Research.Cureus · 2026Article
- Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.Bioengineering (Basel, Switzerland) · 2026Review
- AI-assisted, human-in-the-loop pipeline for converting historical documents into structured database: a feasibility study of environmental application in Michigan.Evidence-based toxicology · 2026Article
- Evaluating AI Performance in Systematic Literature Reviews for HEOR: A Case Study.Journal of health economics and outcomes research · 2026Article
- ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations.Statistics in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.