Reporting guidelines for studies involving generative artificial intelligence applications: what do I use, and when?

The rise in publications addressing the use of general artificial intelligence (GAI), namely large language models (LLMs), for health purposes has generated the need to guide authors on transparent reporting practices 1 , 2 . Although LLMs currently dominate, other GAI applications such as diffusion models and large multimodal models are gaining popularity 3 . One key distinction between GAI and conventional AI is the ability of GAI to create new information based on its training data. Varying methodology and incomplete reporting among studies applying GAI for health purposes compromise the ability of readers to accurately interpret the study findings 3 , which is a particularly relevant issue when evaluating the effectiveness of complex GAI platforms in a healthcare context.