Beyond Pattern Matching: Fine-Tuning Llama 3.2 to Make Healthcare AI Clinically Reliable

General-purpose LLMs often fail in healthcare by pattern-matching surface correlations rather than understanding clinical causality, spreading medical misinformation. Fine-tuning on curated, authoritative datasets like MedQuAD forces models to learn verified clinical pathways instead of relying on token correlations. This approach scales beyond healthcare to any domain requiring expert-level reliability.