This research examines how uncertainty quantification can be integrated into large language models for safer medical applications.

• Reframes uncertainty as a valuable component of medical knowledge rather than a limitation
• Advocates for a dynamic, reflective approach to AI design in clinical settings
• Addresses both technical innovations and philosophical implications of uncertainty in medical AI
• Emphasizes the critical need for transparent communication of AI confidence levels

For healthcare stakeholders, this research provides essential insights into building more reliable AI-assisted clinical decision-making systems that acknowledge their limitations and promote patient safety.

The challenge of uncertainty quantification of large language models in medicine