This systematic review examines how Large Language Models are transforming healthcare text classification, addressing longstanding challenges in clinical documentation processing.

• Evaluates LLM applications in clinical notes analysis and diagnosis coding
• Addresses critical challenges including manual annotation requirements and handling of imbalanced healthcare data
• Highlights the tension between leveraging advanced NLP capabilities and maintaining patient data privacy
• Provides a framework for implementing LLMs in healthcare text classification workflows

This research is significant for healthcare organizations seeking to improve diagnostic accuracy, reduce administrative burden, and enhance clinical decision support while maintaining compliance with privacy regulations.

Large Language Models for Healthcare Text Classification: A Systematic Review