The Mayo Clinic seeks to enhance the reliability of generative AI by implementing innovative techniques that combine algorithms with large language models (LLMs) and vector databases. By employing the CURE algorithm, the Mayo Clinic aims to detect outlier data and ensure that generated summaries are accurately aligned with source documents. This dual-checking approach signifies a shift in how AI tools are monitored, contemplating either increasing human oversight or utilizing AI systems to supervise these generative technologies, while maintaining efficiency in workflows.
Mayo Clinic is pushing back against generative AI problems, combining algorithms with LLMs and vector databases to verify data and ensure accuracy.
Mayo paired CURE algorithm with LLMs and vector databases to double-check data retrieval and detect outliers in generated content.
The Mayo Clinic's method involves splitting generated summaries into individual facts, matching them back to source documents for causal alignment.
Two categories exist for improving genAI reliability: increased human oversight or AI systems monitoring other AI systems.
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