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

Universal healthcare access remains a critical unmet need, especially in resource-limited settings. Large Language Models (LLMs) hold immense promise for democratizing healthcare globally, offering sophisticated diagnostic tools even in remote areas. However, responsible clinical deployment, especially in resource-scarce and trust-dependent environments, demands comprehensive reliability evaluation. This must go beyond accuracy to encompass diagnostic consistency, manipulation resilienceandintelligent contextual integration, ensuring the safe and ethical application of LLMs for universal healthcare.