DOME Copilot: A resource to automate transparent reporting of artificial intelligence methods
Year: 2026
Type: preprint
Abstract: Artificial intelligence (AI) methods are transforming life science research and witnessing unprecedented adoption across literature. While AI is driving impactful discoveries, this rapid growth in application has inundated researchers with poorly described models and datasets which lack transparency, impeding reusability and eroding trust. The DOME Recommendations aimed to address this issue and established structured reporting guidelines to standardize AI methodology descriptions. However, the need to manually create these comprehensive disclosures imposed a major bottleneck, requiring substantial effort from authors to comply. To bridge this gap, we present DOME Copilot, an open-source, easily deployable system that automates the generation of transparent AI methodology reports supplementing publications. Evaluated against a benchmark of human-created annotations, DOME Copilot was determined to match or exceed manual annotation quality across the majority of reporting fields while reducing the creation time from hours to minutes. Ultimately, the system provides a novel and scalable solution to restore confidence in complex life science AI method publications.
Authors: Gavin Farrell, Omar Abdelghani Attafi, Styliani-Christina Fragkouli, Ignacio Heredia, Saul Fernández Tobías, Melissa Harrison, Henning Hermjakob, Matt Jeffryes, Mahta Mehdiabadi, Marta Obregón Ruiz, Matt Pearce, Nikos Pechlivanis, Federica Quaglia, Alvaro López García, Fotis Psomopoulos, Silvio C.E. Tosatto
Institutions: University of Padua, National and Kapodistrian University of Athens, Centre for Research and Technology Hellas, Consejo Superior de Investigaciones Científicas, Instituto de Física de Cantabria, European Bioinformatics Institute, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7154897066