Large language models like GPT-4 and GPT-3.5 show promise in healthcare NLP but face accuracy, cost, and reliability challenges compared to specialized solutions. Benchmarks show domain-specific tools such as IQVIA NLP, Microsoft, and AWS outperform LLMs in key tasks like named entity recognition and MedDRA coding. IQVIA combines fine-tuned BERT models, ontologies, and human-in-the-loop validation through tools like HART to ensure precision and regulatory compliance. While LLMs excel at general tasks, fit-for-purpose blended approaches currently provide higher reliability, scalability, and value in regulated healthcare contexts.

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