This research evaluates the reliability of Large Language Models for medical diagnostics, focusing on consistency, manipulation resistance, and contextual awareness—critical factors for deployment in healthcare settings.

Key Findings:

• LLMs show promise for democratizing healthcare access in resource-limited settings
• Reliability assessment centered on consistency, manipulation resilience, and contextual integration
• Research identifies critical factors for safe and ethical deployment in healthcare
• Establishes evaluation framework for LLM reliability in trust-dependent medical environments

This research matters because it addresses fundamental concerns about AI reliability in high-stakes medical applications, potentially expanding healthcare access while highlighting necessary safeguards.

The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness