Evidence map›Paper›PMID 39919289›Full record

ArticleJournal of medical Internet research2025

ChatGPT for Univariate Statistics: Validation of AI-Assisted Data Analysis in Healthcare Research.

Michael R Ruta, Tony Gaidici, Chase Irwin, Jonathan Lifshitz

Abstract readValidation Study
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
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  4. Article
  5. Review
  6. Review
  7. Article
  8. Statistical analysis using ChatGPT in medical research.Obstetrics & gynecology science · 2025
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Michael R RutaUniversity of Arizona College of Medicine - Phoenix, Phoenix, AZ, United States.ORCID 0009-0003-1246-3360
Tony GaidiciUniversity of Arizona College of Medicine - Phoenix, Phoenix, AZ, United States.ORCID 0009-0001-8175-0713
Chase IrwinUniversity of Arizona College of Medicine - Phoenix, Phoenix, AZ, United States.ORCID 0000-0002-3165-0693
Jonathan LifshitzUniversity of Arizona College of Medicine - Phoenix, Phoenix, AZ, United States.ORCID 0000-0002-4398-6493

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatGPT, a conversational artificial intelligence developed by OpenAI, has rapidly become an invaluable tool for researchers. With the recent integration of Python code interpretation into the ChatGPT environment, there has been a significant increase in the potential utility of ChatGPT as a research tool, particularly in terms of data analysis applications.

objectiveThis study aimed to assess ChatGPT as a data analysis tool and provide researchers with a framework for applying ChatGPT to data management tasks, descriptive statistics, and inferential statistics.

methodsA subset of the National Inpatient Sample was extracted. Data analysis trials were divided into data processing, categorization, and tabulation, as well as descriptive and inferential statistics. For data processing, categorization, and tabulation assessments, ChatGPT was prompted to reclassify variables, subset variables, and present data, respectively. Descriptive statistics assessments included mean, SD, median, and IQR calculations. Inferential statistics assessments were conducted at varying levels of prompt specificity ("Basic," "Intermediate," and "Advanced"). Specific tests included chi-square, Pearson correlation, independent 2-sample t test, 1-way ANOVA, Fisher exact, Spearman correlation, Mann-Whitney U test, and Kruskal-Wallis H test. Outcomes from consecutive prompt-based trials were assessed against expected statistical values calculated in Python (Python Software Foundation), SAS (SAS Institute), and RStudio (Posit PBC).

resultsChatGPT accurately performed data processing, categorization, and tabulation across all trials. For descriptive statistics, it provided accurate means, SDs, medians, and IQRs across all trials. Inferential statistics accuracy against expected statistical values varied with prompt specificity: 32.5% accuracy for "Basic" prompts, 81.3% for "Intermediate" prompts, and 92.5% for "Advanced" prompts.

conclusionsChatGPT shows promise as a tool for exploratory data analysis, particularly for researchers with some statistical knowledge and limited programming expertise. However, its application requires careful prompt construction and human oversight to ensure accuracy. As a supplementary tool, ChatGPT can enhance data analysis efficiency and broaden research accessibility.

Indexed as

Artificial IntelligenceData AnalysisHealth Services ResearchGenerative Artificial IntelligenceHumansartificial intelligencebioinformaticsbiomedical researchchatbotChatGPTdata analysisdata processingprogrammersstatistics

Identifiers

PMID39919289
PMCPMC11845875

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.