Performance of ChatGPT-3.5 and GPT-4 in national licensing examinations for medicine, pharmacy, dentistry, and nursing: a systematic review and meta-analysis

Abstract

Background

ChatGPT, a recently developed artificial intelligence (AI) chatbot, has demonstrated improved performance in examinations in the medical field. However, thus far, an overall evaluation of the potential of ChatGPT models (ChatGPT-3.5 and GPT-4) in a variety of national health licensing examinations is lacking. This study aimed to provide a comprehensive assessment of the ChatGPT models’ performance in national licensing examinations for medical, pharmacy, dentistry, and nursing research through a meta-analysis.

Methods

Following the PRISMA protocol, full-text articles from MEDLINE/PubMed, EMBASE, ERIC, Cochrane Library, Web of Science, and key journals were reviewed from the time of ChatGPT’s introduction to February 27, 2024. Studies were eligible if they evaluated the performance of a ChatGPT model (ChatGPT-3.5 or GPT-4); related to national licensing examinations in the fields of medicine, pharmacy, dentistry, or nursing; involved multiple-choice questions; and provided data that enabled the calculation of effect size. Two reviewers independently completed data extraction, coding, and quality assessment. The JBI Critical Appraisal Tools were used to assess the quality of the selected articles. Overall effect size and 95% confidence intervals [CIs] were calculated using a random-effects model.

Results

A total of 23 studies were considered for this review, which evaluated the accuracy of four types of national licensing examinations. The selected articles were in the fields of medicine (n = 17), pharmacy (n = 3), nursing (n = 2), and dentistry (n = 1). They reported varying accuracy levels, ranging from 36 to 77% for ChatGPT-3.5 and 64.4–100% for GPT-4. The overall effect size for the percentage of accuracy was 70.1% (95% CI, 65–74.8%), which was statistically significant (p < 0.001). Subgroup analyses revealed that GPT-4 demonstrated significantly higher accuracy in providing correct responses than its earlier version, ChatGPT-3.5. Additionally, in the context of health licensing examinations, the ChatGPT models exhibited greater proficiency in the following order: pharmacy, medicine, dentistry, and nursing. However, the lack of a broader set of questions, including open-ended and scenario-based questions, and significant heterogeneity were limitations of this meta-analysis.

Conclusions

This study sheds light on the accuracy of ChatGPT models in four national health licensing examinations across various countries and provides a practical basis and theoretical support for future research. Further studies are needed to explore their utilization in medical and health education by including a broader and more diverse range of questions, along with more advanced versions of AI chatbots.