This comprehensive review examines how Large Language Models (LLMs) are being adapted and evaluated for healthcare applications across 61 recent studies.

• Diverse Training Corpora: Research identified four key data sources: clinical resources, medical literature, open-source datasets, and web-crawled data
• Customization Approaches: Effective strategies include pre-training, prompt engineering, and retrieval-augmented generation, with 71% of studies using multiple methods
• Specialized Evaluation: Healthcare applications require unique metrics beyond traditional NLP measures to ensure clinical accuracy and safety
• Implementation Challenges: The research addresses data fairness issues and biases that impact equitable healthcare delivery

This work provides critical insights for organizations developing AI healthcare solutions, highlighting best practices for creating clinically relevant and reliable LLM applications.

Exploring Large Language Models in Healthcare: Insights into Corpora Sources, Customization Strategies, and Evaluation Metrics