Evidence map›Paper›PMID 41104143›Full record

Trial reportInternational journal of public health2025

Generative Artificial Intelligence for Data Analysis: A Randomised Controlled Trial in a Public Health Research Institute.

Tafadzwa Dhokotera, Nandi Joubert, Aline Veillat, Christoph Pimmer, Karin Gross, Marco Waser, Jan Hattendorf, Julia Bohlius

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in International journal of public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Tafadzwa DhokoteraDepartment Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Associated Institute of the University of Basel, Basel, Switzerland.
Nandi JoubertDepartment Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Associated Institute of the University of Basel, Basel, Switzerland.
Aline VeillatDepartment Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Associated Institute of the University of Basel, Basel, Switzerland.
Christoph PimmerDepartment Education and Training, Swiss Tropical and Public Health Institute, Associated Institute of the University of Basel, Basel, Switzerland.
Karin GrossDepartment Education and Training, Swiss Tropical and Public Health Institute, Associated Institute of the University of Basel, Basel, Switzerland.
Marco WaserSwiss Tropical and Public Health Institute, Associated Institute of the University of Basel, Basel, Switzerland.
Jan HattendorfDepartment Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Associated Institute of the University of Basel, Basel, Switzerland.
Julia BohliusDepartment Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Associated Institute of the University of Basel, Basel, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To assess the competence of students and academic staff to use generative artificial intelligence (GenAI) as a tool in epidemiological data analyses in a randomised controlled trial (RCT). Methods: We invited postgraduate students and academic staff at the Swiss Tropical and Public Health Institute to the RCT. Participants were randomized to analyse a simulated cross-sectional dataset using ChatGPT's code interpreter (integrated analysis arm) vs. a statistical software (R/Stata) with ChatGPT as a support tool (distributed analysis arm). The primary outcome was the trial task score (out of 17, using an assessment rubric). Secondary outcome was the time to complete the task. Results: We invited 338 and randomized 31 participants equally to the two study arms and 30 participants submitted results. Overall, there was no statistically significant difference in mean task scores between the distributed analysis arm (8.5, ±4.6) and the integrated analysis arm (9.4, ±3.8), with a mean difference of 0.93 (p = 0.55). Mean task completion time was significantly shorter in the integrated analysis arm compared to the distributed analysis arm. Conclusion: While ChatGPT offers advantages, its effective use requires a careful balance of GenAI capabilities and human expertise.

Indexed as

Artificial IntelligenceData AnalysisPublic HealthAdultCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMaleSwitzerlandChatGPTdata analysisepidemiologygenerative artificial intelligencehigher education

Identifiers

PMID41104143
PMCPMC12521002

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.