Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.1K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
2.1K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.4K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.4K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

4.1K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
4.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Minimum Sample Size Requirements for Machine Learning: A Study on Diabetic Neuropathy Prediction.

Studies in health technology and informatics·2026
Same author

Inpatient Characteristics and Outcomes of Venous Thromboembolism Among Children and Adolescents.

JAMA network open·2026
Same author

Serum fibroblast growth factor-2 and fibroblast growth factor-9 levels in bipolar disorder: Potential biomarkers for mood episodes.

Psychiatry research·2026
Same author

Predicting Mortality in Older Adults Using Comprehensive Geriatric Assessment: A Comparative Study of Traditional Statistics and Machine Learning Approaches.

Diagnostics (Basel, Switzerland)·2025
Same author

Letter to the Editor concerning "Evaluating ChatGPT-4.0's accuracy and potential in idiopathic scoliosis conservative treatment: a preliminary study on clarity, validity, and expert perceptions" by negrini F, et al. (Eur Spine J [2025]: https://doi.org/10.1007/s00586-025-09166-4).

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2025
Same author

Gut Microbiota and Short-Chain Fatty Acid Profiles in Facioscapulohumeral Dystrophy: Associations with Epigenetic Alterations.

The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques·2025

Related Experiment Video

Updated: Sep 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

692

Using Large Language Models for Data Cleaning: An Evaluation of ChatGPT-4o's Performance.

Nevruz Ilhanli1, Esra Tokur Sonuvar1, Kemal Hakan Gulkesen1

  • 1Biostatistics and Medical Informatics, Faculty of Medicine, Akdeniz University.

Studies in Health Technology and Informatics
|July 1, 2025
PubMed
Summary

Automated data cleaning using ChatGPT-4o shows promise, achieving high accuracy for most variables. Further research is needed to address limitations, especially for complex data like urine glucose.

Keywords:
ChatGPTData CleaningData QualityLarge Language Models (LLMs)

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

592
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.6K

Related Experiment Videos

Last Updated: Sep 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

692
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

592
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.6K

Area of Science:

  • Data Science
  • Artificial Intelligence
  • Health Informatics

Background:

  • Manual data cleaning is essential for data quality but is time-consuming and prone to errors.
  • Automated data cleaning approaches are needed to improve efficiency and accuracy.
  • Large language models like ChatGPT offer potential for automating data cleaning tasks.

Purpose of the Study:

  • To evaluate the performance of ChatGPT-4o in automating data cleaning.
  • To assess the accuracy and consistency of ChatGPT-4o across different data variables.

Main Methods:

  • Utilized ChatGPT-4o for automated data cleaning.
  • Evaluated cleaning performance on gender, hemoglobin, route, and urine glucose variables.
  • Conducted three trials to assess consistency and identify variations.

Main Results:

  • ChatGPT-4o achieved high mean accuracies: 94.3% (gender), 92.5% (hemoglobin), 92.8% (route).
  • Lower accuracy (70.0%) was observed for the urine glucose variable.
  • Consistent accuracy was noted for gender, hemoglobin, and route across trials.
  • Significant variation in accuracy was found for urine glucose across trials.

Conclusions:

  • ChatGPT-4o demonstrates significant potential for automated data cleaning, particularly for structured variables.
  • The performance for complex or variable data (e.g., urine glucose) requires further investigation and refinement.
  • Future research should focus on understanding and mitigating the limitations of AI in data cleaning.