Objectives: Data generation in clinical settings is ongoing and perpetually increasing. Artificial intelligence (AI) software may help detect data-related errors or facilitate process management. The aim of the present study was to test the extent to which the frequently encountered pre-analytical, analytical, and postanalytical errors in clinical laboratories, and likely clinical diagnoses can be detected through the use of a chatbot. Methods: A total of 20 case scenarios, 20 multiple-choice, and 20 direct questions related to errors observed in pre-analytical, analytical, and postanalytical processes were developed in English. Difficulty assessment was performed for the 60 questions. Responses by 4 chatbots to the questions were scored in a blinded manner by 3 independent laboratory experts for accuracy, usefulness, and completeness. Results: According to Chi-squared test, accuracy score of ChatGPT-3.5 (54.4%) was significantly lower than CopyAI (86.7%) (p=0.0269) and ChatGPT v4.0. (88.9%) (p=0.0168), respectively in cases. In direct questions, there was no significant difference between ChatGPT-3.5 (67.8%) and WriteSonic (69.4%), ChatGPT v4.0. (78.9%) and CopyAI (73.9%) (p=0.914, p=0.433 and p=0.675, respectively) accuracy scores. CopyAI (90.6%) presented significantly better performance compared to ChatGPT-3.5 (62.2%) (p=0.036) in multiple choice questions. Conclusions: These applications presented considerable performance to find out the cases and reply to questions. In the future, the use of AI applications is likely to increase in clinical settings if trained and validated by technical and medical experts within a structural framework.