quality_assurance · healthcare · workflow
Genentech develops breakthrough labeling process for medical imagery ML
Genentech's clinical development team needed advanced quality workflows and secure infrastructure to label medical imagery training data at scale, but relying solely on trained medical experts for annotation was costly and time-consuming, limiting their ability to deploy life-saving AI algorithms.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · AI initiative triggers labeling need
Genentech's clinical development team launched AI initiatives in 2019 requiring advanced quality workflows for training data.
Tools used
Labelboxconvolutional neural networks
Outcome
By having domain experts train labelers instead of performing all annotation themselves, Genentech is now producing labeled data that is 10x cheaper, 5x faster, and with better quality, enabling scaled training data operations and faster algorithm deployment.
Results
Volume5x faster
Cost replaced10x cheaper
Running since2019
Grounding & classification
Source type: vendor customer story
22 fields verified against source quotes.
computer visionradiology imagemetric backednamed customerproduction runtime claimedtools describedworkflow describedhealthcarepharma life sciencesaccuracy improvementcost reductioncycle time reductionthroughput increasevendor customer storydata entry opsquality assurancehuman review queue