Spot on, @Christian_Reich
I completely agree that it will ultimately come down to which ecosystem performs best in practice. But as we look ahead to that future, a couple of thoughts stand out from our experience:
1. The Illusion of “Generic” Ontology AI in Medicine
Many tech companies and AI experts talk about applying “ontology AI” to healthcare, drawing parallels to how platforms like Palantir handle ontologies in other industries. But medical ontology is a completely different beast. A massive part of medical education is simply learning the exact definitions and boundaries of clinical terms. I struggle to think of any other field where such a vast, intricate vocabulary exists with globally unified meanings. While there is no shortage of ontology AI experts out there, very few truly understand the clinical nuances of medical ontologies.
2. Medallion Architecture for medical AI"
We also frequently see data engineers pushing the Medallion Architecture (Bronze → Silver → Gold) as the ultimate data strategy. From where I stand, we don’t need to reinvent the wheel—the OMOP CDM is already the Gold Layer. It provides the curated, semantic truth that foundational models can’t just guess their way into.
If an AI model is built on “dirty tokens” without this structured foundational layer, it might look impressive on the surface, but it lacks the reproducible, auditable grounding required for high-stakes clinical research. My gut feeling is that as people start moving legacy DWs into Silver or Gold layers to build agentic AI, they will finally begin to realize the true, indispensable value of the OMOP CDM and standard vocabularies. Ultimately, the models that “beat” generic foundational giants in understanding diseases and outcomes will likely be the ones that embrace, rather than dismiss, this deep medical infrastructure.
To your point about this ongoing debate, our group at Yonsei recently launched a global community survey led by Jiwon Um (PhD candidate) to systematically capture where the OHDSI community stands: “Does data standardization still matter in the era of foundation models, or is it becoming a legacy cost?”
Please visit this thread and participate in the survey