Background The emergence of Large Language Models (LLMs) like ChatGPT presents significant opportunities for healthcare, yet raises concerns about accuracy, especially in high-risk areas such as medication counseling. A comprehensive evaluation of ChatGPT’s reliability in providing drug information is crucial for its safe integration into clinical practice. This systematic review and meta-analysis aimed to assess the accuracy of drug-counseling information provided by ChatGPT 4.

Methods Following PRISMA, we systematically searched PubMed, Embase, Scopus, and Web of Science on May 9, 2025, for original research evaluating the accuracy of ChatGPT (version 4 or newer) in drug-counseling queries. Included studies compared the AI’s output against standard comparators like pharmacists or drug databases. A random-effects meta-analysis was performed to calculate the pooled proportion of accurate responses, and study quality was assessed using a customized Newcastle-Ottawa Scale (NOS).

Results The search identified 17 eligible studies. Of these, 15 were included in the meta-analysis, which showed a pooled accuracy rate of 86% (95% CI: 0.75–0.95). However, significant heterogeneity was observed across studies (I2=98.5%, p<0.0001). Quality of the studies was a concern, with only four studies (24%) rated as high quality. No evidence of publication bias was found (p=0.91).

Conclusion ChatGPT demonstrates substantial promise in drug counseling, with an 86% accuracy rate that surpasses its performance in other medical domains. However, the high heterogeneity and a non-trivial 14% error rate, coupled with methodological weaknesses in the primary literature, indicate that ChatGPT is not yet ready for autonomous clinical use. Its current role should be as a supplementary tool under the strict supervision of qualified healthcare professionals to ensure patient safety.

The authors have declared no competing interest.

This study did not receive any funding

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