Fig. 2: The maturity of each ML technology is tracked via TRL Cards, which we describe in the “Methods” section.

Here is an example reflecting a neuropathology machine vision use-case²², detailed in the “Discussion” section. Note this is a subset of a full TRL Card, which in reality lives as a full document in an internal wiki. Notice the card clearly communicates the data sources, versions, and assumptions. This helps mitigate invalid assumptions about performance and generalizability when moving from R&D to production and promotes the use of real-world data earlier in the project lifecycle. We recommend documenting datasets thoroughly with semantic versioning and tools such as datasheets for datasets⁷⁶, and following data accountability best practices as they evolve (see ref. 81).