Clinical documentation consumes 35-40% of physician time. AI is transforming this. Ambient clinical intelligence: AI (Nuance DAX, Google Ambient Clinical Intelligence, Suki) listens to patient-physician conversation, automatically generates structured clinical notes. Physicians spend 50% less time on documentation. Technical approach: medical-grade speech recognition -> speaker diarization -> medical NLP for symptom/diagnosis/treatment extraction -> SOAP note template population. Clinical NLP for EHR analysis: 1) Medical NER: identify symptoms, diagnoses, medications, procedures from unstructured notes (specialized BERT models like BioBERT, ClinicalBERT). 2) Temporal event extraction: understand when symptoms appeared, when medications started. 3) Negation detection: "patient denies chest pain" vs "chest pain present" - critical for clinical accuracy. ICD-10/SNOMED coding: fine-tuned transformer for multi-label classification of clinical notes into diagnosis codes. Reduces manual coding time 60-80% with high accuracy (92-95% for common codes). Clinical decision support: integrate AI into clinical workflow to flag potential drug interactions, suggest evidence-based treatment protocols, identify high-risk patients for intervention. HIPAA/GDPR compliance: PHI de-identification before model training, Business Associate Agreements with AI vendors, audit logging of all AI interactions with patient data.