Artificial intelligence meets dairy cow research: Large language model's application in extracting daily...

This study investigates the application of ChatGPT-4 in extracting and classifying behavioral data from scientific literature, focusing on the daily time-activity budget of dairy cows. Accurate analysis of time-activity budgets is crucial for understanding dairy cow welfare and productivity. Traditional methods are time-intensive and prone to bias. This study evaluates the accuracy and reliability of ChatGPT-4 in data extraction and data categorization, considering explicit, inferred, and ambiguous labels for the data, compared with human analysis. A collection of 55 papers on dairy cow behavior were used in the studies. Data extraction for eating, ruminating, and lying behaviors was performed manually and via ChatGPT-4. The artificial intelligence (AI) model's accuracy and labeling performance were assessed through descriptive and statistical analyses. Mixed model analysis was used to compare human and AI outcomes. Artificial intelligence and human time budget data showed significant differences for eating and ruminating but not for lying. ChatGPT-4 estimated daily eating time at 22.3% compared with 23.8% by humans. For ruminating, AI reported 33.4% against 31.7% by humans. Daily lying times were nearly identical, with AI at 44.4% and human analysis at 44.2%. The global accuracy in data extraction was ∼75%, and labeling accuracy reached 67.3%, with significant variability across behavioral categories. In general, the AI model demonstrates moderate accuracy in extracting and categorizing behavioral data, particularly for inferred and ambiguous data. However, explicit data extraction posed challenges, highlighting AI's dependence on input quality and structure. The consistency between AI and human analyses for lying behavior underscores AI's potential for specific applications. ChatGPT-4 offers a promising complementary tool for behavioral research, enabling efficient and scalable data extraction. However, improvements in AI algorithms and standardized reporting in scientific literature are essential for broader applicability. The study advocates for hybrid approaches combining AI capabilities with human oversight to enhance the reliability and accuracy of dairy cow behavioral research.