This comprehensive survey examines the critical challenges of deploying Large Language Models in healthcare settings and proposes frameworks to ensure trustworthiness.

• Truthfulness challenges: LLMs can generate misleading medical information that impacts clinical decisions
• Privacy concerns: Models risk retaining sensitive patient data through unintentional memorization
• Robustness requirements: Healthcare LLMs need robust defenses against adversarial attacks
• Ethical deployment: Frameworks needed to ensure reliable, fair, and transparent use in medical contexts

This research is crucial for healthcare organizations seeking to leverage AI while maintaining clinical standards, patient trust, and regulatory compliance. Proper implementation of these trustworthiness principles can help realize LLMs' potential to transform patient care and medical research.

A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare