Most multimodal healthcare AI projects fail before production due to architectural fragmentation, not modeling limitations. This post presents a production-ready lakehouse blueprint on Databricks for unifying genomics, medical imaging, clinical notes, and wearables data. It covers ingesting each modality into governed Delta tables via Unity Catalog, using Glow for distributed genomics processing, vector search for imaging similarity queries, Lakeflow SDP for streaming wearables pipelines, and four fusion strategies (early, intermediate, late, attention-based). A key emphasis is designing for data sparsity by default, since missing modalities are the norm in clinical deployments, not the exception.
Table of contents
What “governed” means in practiceWhy multimodal is becoming the defaultFour fusion strategies (and when each survives production)The lakehouse as a multimodal substrateWhy the unified storage + governance model mattersSolving the missing modality problemPrecision oncology pattern: from architecture to clinical workflowBusiness impact: what changes when multimodal becomes operationalGet started: a pragmatic first 30 daysSort: