AI benchmarking essential for reliable biomedical research

As artificial intelligence becomes deeply integrated into life sciences research—including genome interpretation, protein structure prediction, and drug discovery—the scientific community is facing challenges regarding the accuracy and reproducibility of these models.

In a recently published perspective, researchers including Julio Saez-Rodriguez of EMBL-EBI and colleagues from NYU discuss the necessity of benchmarking biomedical foundation models. Benchmarking involves the structured and systematic assessment of how accurately an algorithm can answer specific biological questions. Without these standardized assessments, it is difficult to compare models effectively or ensure that predictions reflect genuine biological insights.

To address these needs, Julio Saez-Rodriguez is a co-founder and member of the governance committee of BEACON (Benchmarking, Evaluation, and Assessment Consortium for Science), a new global initiative dedicated to AI benchmarking.