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Building Trustworthy AI: Clinia’s Framework for Safer Answers

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Other possible titles:

• Clinia AI Evaluation Framework for Generative Responses
• Making AI Answers Safer in Health Care
• Clear, Correct, Safe: Setting Standards for AI in Health Care
• Why AI in Health Care Needs More Than Just Smart Words </aside>

When we ask an AI system a question, it can sometimes give an answer that sounds convincing but isn’t backed up by the right information. In everyday situations, this might not matter much, but in health care—where accuracy and trust are essential—the stakes are much higher.

One promising approach to reduce this risk is called Retrieval-Augmented Generation (RAG). Instead of relying only on what it has memorized during training, a RAG system first searches trusted sources—like medical guidelines or scientific articles—and then uses that information to generate its response. In other words, it doesn’t just invent an answer; it looks things up before speaking.

This design makes RAG especially valuable in health care, where knowledge is constantly evolving and every answer must be both reliable and safe. But it also highlights why careful evaluation is necessary: we need to ensure the system retrieves the right sources, interprets them correctly, and communicates its conclusions in a way health professionals can trust. Evaluating RAG in this context is about much more than performance—it’s about ensuring technology truly supports better decisions and, ultimately, better care.

Why Traditional Evaluation Methods Fall Short

Most standard AI evaluation methods come from older natural language processing (NLP) research. Metrics like BLEU or ROUGE compare the AI’s answer to a reference answer by counting word overlaps. This may work well for tasks like translation or summarization but quickly shows its limits for generative AI in health care (Novikova et al., 2017).

• First, these metrics only measure word overlap, not whether the answer is actually correct or meaningful.
• They cannot detect when the AI hallucinates information, uses the wrong tone, produces unclear text, or even generates potentially harmful advice.
• They also tend to penalize answers that are phrased differently, even if they are equally correct.
• Most importantly, they were never designed for high-risk medical content, where accuracy, clarity, and safety of each answer matter more than word similarity.

For this reason, we need a different approach. At Clinia, we have designed our own set of medical and linguistic criteria to evaluate responses from both the content perspective (Is the information correct, complete, and safe?) and the form perspective (Is it clear, respectful, and useful to the reader?). This ensures that our evaluation reflects trustworthiness and patient safety.

Content Quality Guidelines

These guidelines ensure the semantic validity and scientific robustness of model outputs, which are the foundation of trustworthiness in health care AI.

Relevance of the Response