Comparing the performance of ChatGPT and Chatsonic on PLAB-style questions: a cross-sectional study

Abstract

Background: Artificial Intelligence (AI), particularly large language models like ChatGPT and Chatsonic, has garnered significant attention. These models, trained on massive datasets, generate human-like responses. Studies have assessed their performance on professional and licensing examinations, as well as medical examinations, with varying levels of competency. Assessment of ChatGPT and ChatSonic's competence in addressing PLAB-oriented queries. Method: We conducted an independent cross-sectional study in May 2023 to evaluate the performance of ChatGPT and Chatsonic on the PLAB-1 Exam. The study used 180 multiple-choice questions from a mock test on the 'Pastest' platform and excluded questions with images, tables, or unanswered by AI. The responses of the two AI models, correct answers, and question difficulty statistics were recorded and compared. The performance of the two AI software packages was assessed based on the recorded metrics. Results: Out of 180 questions, 141 were included and 39 excluded. ChatGPT outperformed Chatsonic, answering 78% of questions correctly compared to the latter's 66%. ChatGPT achieved 85% accuracy in answering easy questions, while Chatsonic performed poorly across all levels, answering 75% of easy questions, 64% of average questions, and only 38% of difficult questions. Conclusions: ChatGPT outperformed Chatsonic in all dataset categories and showed non-statistically significant superior performance across difficulty levels. Both AI models' accuracy decreased with increasing question difficulty.

Ähnliche Arbeiten