[A Guide to Network Meta-Analysis Using Generative AI and No-Code Tools].

Network meta-analysis (NMA), an increasingly appealing method of statistical analysis, is superior to traditional analysis methods in terms of being able to compare multiple medical treatment methods in one analysis run. In recent years, the prevalence of NMA in the medical literature has increased significantly, while advances in NMA-related statistical methods and software tools continue to improve the effectiveness of this approach. Various commercial and free statistical software packages, some of which employ generative artificial intelligence (GAI) to generate code, have been developed for NMA, leading to numerous innovative developments. In this paper, the use of generative AI for writing R programming language scripts and the netmeta package for performing NMA are introduced. Also, the web-based tool ShinyNMA is introduced. ShinyNMA allows users to conduct NMA using an intuitive "clickable" interface accessible via a standard web browser, with visual charts employed to present results. In the first section, we detail the netmeta package documentation and use ChatGPT (chat generative pre-trained transformer) to write the R scripts necessary to perform NMA with the netmeta package. In the second section, a user interface is developed using the Shiny package to create a ShinyNMA tool. This tool provides a no-code option for users unfamiliar with programming to conduct NMA statistical analysis and plotting. With appropriate prompts, ChatGPT can produce R scripts capable of performing NMA. Using this approach, an NMA analysis tool is developed that meets the research objectives, and potential applications are demonstrated using sample data. Using generative AI and existing statistical packages or no-code tools is expected to make conducting NMA analysis significantly easier for researchers. Moreover, greater access to results generated by NMA analyses will enable decision-makers to review analysis results intuitively in real-time, enhancing the importance of statistical analysis in medical decision-making. Furthermore, enabling non-specialists to conduct clinically meaningful systematic reviews may be expected to sustainably improve analytical capabilities and produce higher-quality evidence.