A 125M model beat a 14B LLM at de-identifying medical text 40 faster, on CPU
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Your data never leaves the machine - and you can check my math Building localscrub, a local-first PHI de-identification cascade, and benchmarking it honestly against the standard baseline - on one consumer laptop, with zero real patient data. De-identifying clinical text today forces a bad trade. Cloud de-id APIs are accurate, but you send the sensitive data out in order to scrub it - the text crosses your trust boundary before a single character is redacted. Local rule-based tools keep the…
1Key Takeaways
- Your data never leaves the machine - and you can check my math Building localscrub, a local-first PHI de-identification cascade, and benchmarking it honestly against the standard baseline - on one consumer laptop, with zero real patient data.
- De-identifying clinical text today forces a bad trade.
- Cloud de-id APIs are accurate, but you send the sensitive data out in order to scrub it - the text crosses your trust boundary before a single character is redacted.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that your data never leaves the machine - and you can check my math Building localscrub, a local-first PHI de-identification cascade, and benchmarking it honestly against the standard baseline - on one consumer laptop, with zero real patient data.
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