EV-Checklist: AI-Powered Rapid Documentation for Enhancing Transparency and Accessibility of Extracellular Vesicle Research Data.

Transition of extracellular vesicle (EV) research from basic discovery to clinical application raised hopes regarding diagnostic, therapeutic and prognostic progress. Rigorous reporting of experimental details is required to align the EV field with pharmaceutical quality standards. MISEV2023 recommendations encourage concise reporting but widespread adoption remains limited. Current reporting tools are time-consuming, and adherence declines despite rapidly growing number of EV studies. We therefore created EV-Checklist, a complementary digital tool that streamlines reporting and increases transparency. By uploading manuscript text, an AI-assisted algorithm automatically completes a checklist covering EV nomenclature, source, isolation, characterization and function. To ensure accuracy, users validate AI-generated entries before submission-ideally, gaps in reporting can be closed (e.g., missing particle/protein ratio). The resulting concise report can accompany manuscripts helping editors, reviewers and readers by presenting key methodological and results details at a glance. EV-Checklist complements existing comprehensive registries as 'fast-and-easy' tool enhancing clarity and accessibility of EV research data and may promote higher adherence to documentation standards. Adoption may be encouraged by journal endorsement to streamline the review process for compliant submissions, signalling adherence to MISEV2023 standards. EV-Checklist and an accompanying AI-assisted search tool (PMC EV Search), spanning over 45,700 open-access EV manuscripts, are publicly available at https://ev-zone.org/.