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Glossary Definition
Natural Language Processing (NLP) in Medicine
Quick Answer
Natural language processing (NLP) in medicine is the branch of artificial intelligence that enables computers to understand, interpret, and generate human language in clinical contexts — powering applications from clinical documentation to medical literature search to conversational clinical decision support.
Source: The Clinical AI Report, February 2026
Definition
Medical NLP applies natural language processing techniques to healthcare-specific text, including clinical notes, medical literature, patient communications, and physician queries. NLP enables clinical AI tools to accept free-text questions from physicians (rather than requiring structured inputs), extract clinical information from unstructured EHR notes, and generate human-readable medical text. It is the foundational technology behind ambient AI scribes, conversational clinical decision support, and medical literature synthesis tools.
Key NLP Applications in Healthcare
NLP powers several critical healthcare applications: (1) Clinical documentation — converting speech to structured clinical notes using automatic speech recognition and language understanding, (2) Medical information extraction — pulling diagnoses, medications, and symptoms from unstructured clinical text, (3) Clinical decision support — enabling physicians to query medical knowledge bases using natural language, (4) Literature synthesis — automatically summarizing relevant research papers for clinical questions, (5) Patient communication — generating patient-friendly explanations of medical conditions and treatment plans.
From Rule-Based NLP to Large Language Models
Early medical NLP relied on hand-crafted rules and medical ontologies like SNOMED CT and UMLS to extract structured data from clinical text. Modern medical NLP is increasingly powered by large language models (LLMs) that learn language understanding from vast text corpora. This evolution has dramatically expanded what medical NLP can do — from extracting predefined data elements to understanding complex clinical queries, reasoning across evidence, and generating coherent medical text. However, the shift to LLM-based NLP also introduced new risks, particularly hallucination.
Challenges in Medical NLP
Medical NLP faces unique challenges compared to general-purpose NLP: (1) Clinical text uses specialized terminology, abbreviations, and shorthand that varies between institutions, (2) Negation detection is critical — 'no fever' must be distinguished from 'fever,' (3) Temporal reasoning matters — medications 'discontinued last week' have different clinical significance than current medications, (4) Ambiguity is common — 'cold' could mean a temperature, a respiratory infection, or a sensation, (5) Errors carry higher consequences than in most other NLP applications because they can affect patient care.
Written by The Clinical AI Report editorial team. Last updated February 15, 2026.