ArticleSaudi medical journal2024
Assessing the accuracy and efficiency of Chat GPT-4 Omni (GPT-4o) in biomedical statistics: Comparative study with traditional tools.
Article in Saudi medical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Can large language models accurately compute descriptive statistics from structured datasets? A comparative evaluation of ChatGPT and Claude.American heart journal plus : cardiology research and practice · 2026Article
- Large language models are comparable with commonly used statistical software: A validation of GPT 5.1 for frequentist meta-analysis in orthopaedics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- Article
- Statistical Consistency in Artificial Intelligence-Assisted Computations: A Comparison of SPSS 31.0 and GPT-5.5.Cureus · 2026Article
- Evaluation of large language models in medical examinations: A scoping review protocol.PloS one · 2026Article
- Exploring the role of DeepSeek-R1, ChatGPT-4, and Google Gemini in medical education: How valid and reliable are they?Pakistan journal of medical sciences · 2025Article
- Article
- Promising Results About the Possibility to Identify Prostate Cancer Patients Employing a Random Forest Classifier: A Preliminary Study Preoperative Patients Selection.Diagnostics (Basel, Switzerland) · 2025Article
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3 authors.
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Abstract
objectivesTo assess the accuracy of ChatGPT-4 Omni (GPT-4o) in biomedical statistics. The recent novel inauguration of Artificial Intelligence ChatGPT-Omni (GPT-4o), has emerged with the potential to analyze sophisticated and extensive data sets, challenging the expertise of statisticians using traditional statistical tools for data analysis.
methodsThis study was performed in the Department of Physiology, College of Medicine, King Saud University, Riyadh, Saudi Arabia, in May 2024. Three datasets in a raw Excel file format were imported onto Statistical Package for the Social Sciences (SPSS) version 29 for data analysis. Based on this analysis, a script of 9 questions was prepared to command GPT-4 Omni, which was used for data analysis for all 3 datasets on Omni. The score and the time were recorded for each result and verified after being compared to the original analysis results performed on SPSS.
resultsGPT-4 Omni scored 73 (85.88%) out of 85 points for all 3 datasets. All datasets took a total of 38.43 minutes to be fully analyzed. Individually, Omni scored 21/25 (84%) for the small dataset in 487.4 seconds, 20/25 (80%) for the middle dataset in 747.02 seconds and 32/35 (91.42%) for the large dataset in 1071 seconds. GPT-4 Omni produced accurate graphs and charts.
conclusionChatGPT-4 Omni scored better over 80% in all 3 statistical datasets in a short period. GPT-4 Omni also produced accurate graphs and charts as commanded however it required explicit commands with clear instructions to avoid errors and omission of results to achieve appropriate results in biomedical data analysis.
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