ReviewNature methods2026
Benchmarking biomedical foundation models.
Review in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A transparent evaluation and proof of reproducibility, generalization and replicability of algorithms are the bedrock of method development in computational biology. Many benchmarking efforts have been developed for problems ranging from structural biology to translational biomedicine. Rigor is relatively controllable for tasks such as the prediction of patient outcomes or the outcomes of biological assays, but the problem is exacerbated when the aim is to benchmark foundation models. The parameters constituting them are supposed to capture the patterns underlying the data; therefore, the models are parameterized embodiments of the phenomena that gave rise to the data. How can we test the limitations of these models? Here, we discuss the epistemological value of foundation models; whether they can be refuted, verified or evaluated primarily on the basis of utility; what principles should guide their benchmarking; and what role the scientific community should play in that benchmarking process.
Indexed as
Identifiers
42697996What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.