Building Trustworthy Voice AI for Pharma: A Deep Dive Into Our Evaluation Framework

At SynthioLabs, our mission in building Medical-grade Voice-first AI agents goes far beyond answering questions — we’re focused on earning trust.

In Life Sciences, every interaction with a medical professional carries weight. When a healthcare professional (HCP) asks about clinical trial data, dosing protocols, or safety risks, the response isn’t just informational — it’s clinical, scientific, and must be delivered with accuracy, nuance, and professionalism.

To meet this bar, we developed a robust evaluation framework that does more than check if an answer is “correct.” It assesses what was said, how it was said, and whether the exchange meets the expectations of a real-world medical interaction.

What We Evaluate

Each conversation between an AI agent and an HCP is evaluated along two key dimensions:

• Content Quality: Was the response clear, accurate, complete, and clinically sound?
• Voice Delivery: Was the response delivered in a way that felt natural, timely, and professional?

By combining these perspectives, we ensure that our agents are not only smart but also credible, usable, and compliant in a healthcare setting.

Part 1: Evaluating Content Quality

We break down each conversation into distinct segments, each representing a topic or question. For each segment, we assess several attributes:

1.1. Did the AI Answer the Question?

This measures whether the AI directly addressed the HCP’s query. We categorize the responses as:

• Yes – Fully addressed the question
• Partially – Some relevant information, but incomplete
• No – Missed or misunderstood the query

This metric is a fast indicator of relevance and grounding.

1.2. Accuracy and Completeness

These two dimensions are scored on a 1–5 scale:

• Factual Correctness: Was the factual content correct?
• Completeness: Did the response provide all the relevant information needed for clinical understanding?

We benchmark these against trusted sources: prescribing information (PI), clinical trial data, and internal regulatory guidance.

1.3. Answerability

We also evaluate whether the question could reasonably be answered by the AI, given its knowledge base and document access. If not, it highlights areas where our model or retrieval needs to improve — whether it’s a missing clinical detail, a formatting issue, or a knowledge gap.

Advanced Content Metrics

In real-world clinical interactions, technical accuracy isn’t enough. We’ve added deeper qualitative measures to capture the experience of interacting with the AI.

1.4. Tone & Empathy

Was the AI’s tone respectful, professional, and human-like?

We evaluate whether the agent speaks in a way that builds rapport and trust — especially important when discussing sensitive medical topics. A scripted or robotic tone can undermine even a factually perfect response.

1.5. Regulatory Compliance

This metric checks whether the AI stays within regulatory boundaries — no off-label claims, no deviation from approved language, and full alignment with medical-legal standards.

Responses are reviewed against label-approved content, ensuring all interactions remain compliant with industry regulations.

1.6. Context Awareness

We test the AI’s ability to carry multi-turn conversations: Does it maintain coherence, respond appropriately to follow-ups, and avoid contradictions?

This involves tracking the conversational thread across multiple exchanges, scoring how well the agent maintains state and relevance throughout.

Part 2: Evaluating Voice Delivery

Even the best content can be undermined by poor audio delivery. That’s why we evaluate voice-specific metrics with the same rigor.

2.1. Word Error Rate (WER)

We use speech-to-text (STT) to transcribe the AI’s spoken output and compare it to the original script. WER helps quantify how clearly the audio was delivered, including issues with enunciation, pacing, or artifacts.

2.2. Mispronunciations

We specifically check for correct pronunciation of drug names, disease terms, and complex medical vocabulary. Errors here are not only jarring but potentially dangerous — especially with similar-sounding medications.

These are reviewed both manually, with pronunciation dictionaries and compared to spectograms of actual pronunciations to flag deviations from accepted standards.

2.3. Latency

This measures the time from when an HCP finishes speaking to when the AI begins responding. Low latency ensures the exchange feels smooth and conversational.

If latency is too high, the interaction feels robotic or unresponsive — which can erode the sense of dialogue and natural flow.

Why This Framework Matters

We didn’t build this evaluation system to hit a technical benchmark. We built it because healthcare professionals can’t afford “almost right.”

Every response from our AI must meet both scientific and human standards. It must be accurate, compliant, and context-aware; but also empathetic, timely, and trustworthy. By holding our systems accountable across this wide spectrum, we’re not just building bots. We’re building credible medical partners.

Because in voice, as in medicine, the how is just as important as the what.

Want to Learn More?

If you’re curious about how we measure, refine, and improve our AI agents in the field — we’d love to talk. This framework is the backbone of every interaction we support, and we believe it’s a critical step toward truly reliable voice AI in healthcare.