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Measuring quality and accuracy of responses in ClinicalKey AI
Addressing bias and hallucinations is key in healthcare AI, with regular, rigorous evaluations involving clinicians and data experts ensuring responses are accurate, safe, and trustworthy. This ongoing process helps build confidence in AI tools' reliability and safety.
In this installment of the Visionary Voices series, data scientist Leah Livingston shares how metrics like accuracy and potential harmfulness are important factors in determining the effectiveness of responses provided in ClinicalKey AI.
By empowering healthcare professionals with intelligent tools, we're creating pathways to smarter diagnostics, safer treatments, and truly personalized patient care.
Measuring quality and accuracy of responses in ClinicalKey AI
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