What ethical and privacy issues arise using NLP on patient records

Key issues include

(1) privacy and legal compliance (HIPAA/GDPR): collect minimal necessary data, obtain explicit consent, and apply robust de-identification;
(2) re-identification risk: even de-identified text can leak identity via rare events—monitor and audit;
(3) utility–privacy trade-off: redaction can degrade model performance, so justify processing with data-protection impact assessments;
(4) bias and fairness: language models may encode demographic biases affecting care—perform subgroup analyses and mitigation;
(5) transparency and accountability: document datasets, labeling, and intended use; ensure models support clinicians rather than replace them;
(6) data security and access governance for transcripts and models.

Continuous monitoring, human review, and clear patient communication about secondary uses are essential. Empirical studies show both the feasibility of certified de-identification and its measurable impact on downstream tasks—reinforcing the need for rigorous, ethical ML operations.