Integration with biomedical data systems#3
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blengerich merged 5 commits intoAdaptInfer:mainfrom May 4, 2026
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Expanded the section on integrating foundation models with biomedical data systems, detailing various integration paradigms and their challenges.
Removed section on open challenges in multimodal integration.
delete the outline and simplify the main body of five integrations
hjung-stat
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Apr 19, 2026
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| This perspective is closely related to the central question of this review: whether biomedical tasks are better addressed by domain-specific foundation models or by adapting general foundation models. Domain-specific models are often designed with biomedical data structures in mind, whereas general models typically require additional adaptation strategies, such as prompting, retrieval, or tool use, to interact effectively with structured biomedical information. Therefore, integration with biomedical data systems provides a useful lens for comparing the practical strengths and limitations of these two approaches [@doi:10.1093/jamia/ocae074; @doi:10.1093/jamia/ocae202]. | ||
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| Among biomedical data systems, EHRs are especially important in clinical AI. They include structured information such as diagnosis codes, medications, procedures, and laboratory measurements, as well as semi-structured or unstructured components such as clinical notes and discharge summaries. EHR data is inherently longitudinal, sparse, noisy, and irregularly sampled, which makes it substantially different from the text corpora or image datasets on which many foundation models are pretrained [@doi:10.1038/sdata.2016.35]. Biomedical ontologies and controlled vocabularies provide another key layer of structure. Resources such as SNOMED CT, UMLS, and Gene Ontology support normalization, interoperability, and structured reasoning across datasets and institutions [@doi:10.1093/nar/gkh061; @doi:10.1093/nar/gkaa1113; @doi:10.2196/62924]. Biomedical workflows also depend heavily on curated databases and knowledge resources, including literature repositories, population-scale datasets, molecular interaction resources, and disease-specific knowledge bases. In many practical settings, the usefulness of a biomedical model depends not only on what it stores internally, but also on how effectively it can access and use such external resources [@doi:10.1093/jamia/ocaf008]. |
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"Irregularly sampled” might be a bit unclear here, perhaps you could briefly clarify that EHR data is collected based on clinical need rather than a fixed schedule.
Added an overview section discussing the interaction between biomedical AI models and data systems, highlighting the comparison between domain-specific and general foundation models.
Expanded on the challenges of integrating EHRs with foundation models, highlighting their longitudinal, sparse, noisy, and irregularly timed nature.
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[ci skip] This build is based on c85cb44. This commit was created by the following CI build and job: https://github.com/AdaptInfer/fm-survey/commit/c85cb449469b31b6b728eed65453617f7993d4ce/checks https://github.com/AdaptInfer/fm-survey/actions/runs/25328693282
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[ci skip] This build is based on c85cb44. This commit was created by the following CI build and job: https://github.com/AdaptInfer/fm-survey/commit/c85cb449469b31b6b728eed65453617f7993d4ce/checks https://github.com/AdaptInfer/fm-survey/actions/runs/25328693282
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Expanded the section on integrating foundation models with biomedical data systems, detailing various integration paradigms and their challenges.