BACKGROUND: Meta-analysis is a critical tool in evidence-based medicine, particularly in cardiology, where it synthesizes data from multiple studies to inform clinical decisions. This study explored the potential of using ChatGPT to streamline and enhance the meta-analysis process. AIM: To investigate the potential of ChatGPT to conduct meta-analyses in interventional cardiology by comparing the results of ChatGPT-generated analyses with those of randomly selected, human-conducted meta-analyses on the same topic. METHODS: We systematically searched PubMed for meta-analyses on interventional cardiology published in 2024. Five meta-analyses were randomly chosen. ChatGPT 4.0 was used to perform meta-analyses on the extracted data. We compared the results from ChatGPT with the original meta-analyses, focusing on key effect sizes, such as risk ratios (RR), hazard ratios, and odds ratios, along with their confidence intervals (CI) and values. RESULTS: The ChatGPT results showed high concordance with those of the original meta-analyses. For most outcomes, the effect measures and values generated by ChatGPT closely matched those of the original studies, except for the RR of stent thrombosis in the Sreenivasan study, where ChatGPT reported a non-significant effect size, while the original study found it to be statistically significant. While minor discrepancies were observed in specific CI and values, these differences did not alter the overall conclusions drawn from the analyses. CONCLUSION: Our findings suggest the potential of ChatGPT in conducting meta-analyses in interventional cardiology. However, further research is needed to address the limitations of transparency and potential data quality issues, ensuring that AI-generated analyses are robust and trustworthy for clinical decision-making.