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[TAGALOG] Why LLMs Miss 50% of Clinical PHI...

[TAGALOG] A 2025 study found LLMs miss more than 50% of clinical PHI in multilingual documents. 34.8% of all ChatGPT inputs contain sensitive data.

April 2, 20269 min basahin
LLM PHI detectionHIPAA de-identificationclinical NLPSafe Harbor methodhealthcare AI compliance

[TAGALOG]

The 50% Miss Rate Problem

A 2025 survey of LLM-based de-identification tools (arXiv:2509.14464) found that general-purpose LLM tools miss more than 50% of clinical PHI in multilingual documents. This figure reflects a fundamental architectural mismatch: LLMs are designed for language understanding and generation, not for the structured, high-recall identification task that HIPAA de-identification requires.

The HIPAA Privacy Rule's Safe Harbor method requires removal of 18 specific iden...

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