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Article summary:
1. This study evaluated the performance of ChatGPT, a 175-billion-parameter natural language processing model, on questions from the United States Medical Licensing Examination Step 1 and Step 2 exams.
2. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance.
3. The model demonstrated a significant decrease in performance as question difficulty increased, and provided logical justification for its answer selection in 100% of outputs of the NBME data sets.
Article analysis:
The article is generally trustworthy and reliable, as it provides evidence to support its claims and presents both sides of the argument fairly. The authors provide detailed information about their methodology and results, which allows readers to evaluate the accuracy of their findings. Additionally, they cite relevant research to back up their claims and provide an analysis of potential implications for medical education and knowledge assessment.
However, there are some potential biases that should be noted. For example, the authors only used two sets of multiple-choice questions to evaluate ChatGPT's performance; thus, it is possible that other types of questions may yield different results. Additionally, while the authors note that ChatGPT achieved a passing score on the NBME-Free-Step1 data set, they do not provide any information about how this score compares to actual medical students taking the exam or what percentage of medical students would pass with this score. Furthermore, while they discuss potential applications for ChatGPT as an interactive medical education tool, they do not explore any potential risks associated with using such a tool in medical education or knowledge assessment contexts.