AI in biocuration: challenges, opportunities, and a roadmap for sustainable integration

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Abstract
Motivation: Biocuration is the integration of biological information databases for the enhancement of research. Curation of these databases is challenged by the exponential growth of scientific data and literature. Integration of machine learning and artificial intelligence methods into biocuration workflows may help address this challenge. We report on the discussions, ideas, and recommendations gathered from a workshop “AI and biodata resources: implications for sustainability and best practices in biocuration” at the 18th Annual International Biocuration Conference 2025. Results: Participants agreed that while AI offers transformative potential for efficiency and expanded curatorial capacity, its integration faces substantial hurdles. Key challenges revolve around data and model quality. Reproducibility issues and a lack of open, domain-specific training datasets further compound these problems. Broader concerns include inconsistent data standards, underdeveloped ontologies, unstructured data, legacy systems, and underfunded teams. Despite these issues, several successful AI applications were identified, including tools for literature summarization and workflow assistance. Participants emphasized the need for a refined model of human-AI collaboration requiring clear data provenance and transparency, new skills, and avoiding over-reliance on AI-generated data. The workshop ultimately recommended concerted efforts in infrastructure development, standardization, training, and quality assurance to guide the community toward effective human-AI collaboration that maintains scientific rigor.
Funding Information
  • National Institutes of Health–National Human Genome Research Institute (1U24HG012557-01)

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