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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Jul 30·5 min read
The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness
TL;DR
This study assessed the diagnostic reliability of Google Gemini 2.0 Flash and OpenAI ChatGPT-4o in medical scenarios, revealing both models showed 100% consistency but varied in handling irrelevant details and contextual changes. Clinician oversight is essential for safe diagnostic use of LLMs.
✦ Why It Matters
Implement structured oversight when using LLMs for medical diagnosis to ensure accuracy and safety.
Key Takeaways
- Ensure clinician oversight when using LLMs for medical diagnosis.
- Test LLMs with varied clinical scenarios to assess reliability.
- Be cautious of irrelevant details affecting LLM diagnoses.
- Evaluate context-driven changes for clinical appropriateness.
How It Works
The study involved creating clinical scenarios and systematically modifying them to test LLM responses. By rephrasing inputs and embedding irrelevant details, researchers assessed how well the models maintained diagnostic accuracy and adapted to context.
⚠ The Catch
Despite high consistency, both models showed vulnerability to irrelevant input manipulation, leading to unjustified diagnostic shifts when context changed.