Generative AI as a Tool for Environmental Health Research Translation

Generative artificial intelligence, popularized by services like ChatGPT, has been the source of much recent popular attention for publishing health research. Another valuable application is in translating published research studies to readers in non-academic settings. These might include environmental justice communities, mainstream media outlets, and community science groups. Five recently published (2021-2022) open-access, peer-reviewed papers, authored by University of Louisville environmental health investigators and collaborators, were submitted to ChatGPT. The average rating of all summaries of all types across the five different studies ranged between 3 and 5, indicating good overall content quality. ChatGPT’s general summary request was consistently rated lower than all other summary types. Whereas higher ratings of 4 and 5 were assigned to the more synthetic, insight-oriented activities, such as the production of a plain language summaries suitable for an 8th grade reading level and identifying the most important finding and real-world research applications. This is a case where artificial intelligence might help level the playing field, for example by creating accessible insights and enabling the large-scale production of high-quality plain language summaries which would truly bring open access to this scientific information. This possibility, combined with the increasing public policy trends encouraging and demanding free access for research supported with public funds, may alter the role journal publications play in communicating science in society. For the field of environmental health science, no-cost AI technology such as ChatGPT holds the promise to improve research translation, but it must continue to be improved (or improve itself) from its current capability. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement We would like to acknowledge the support of the Superfund Research Center at the University of Louisville (NIEHS Award Number P42ES023716). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The list of peer-reviewed research papers analyzed during the current study are available in the supplement to this manuscript.