intentguard-healthcare

IntentGuard — Healthcare & Clinical

Production-ready vertical intent classifier for LLM chatbot guardrails. Classifies user messages as allow, deny, or abstain to keep healthcare chatbots on-topic and compliant.

Research Article | perfecXion.ai | Finance Model | Healthcare Model | Legal Model

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IntentGuard Model Family

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Overview

The Problem

Healthcare chatbots face unique regulatory risks — HIPAA compliance, patient safety, and liability exposure demand that AI assistants stay strictly within clinical domains. Users asking about sports scores, celebrity news, or relationship advice should be blocked, not answered.

The Solution

IntentGuard uses a tiny DeBERTa-v3-xsmall model (22M parameters, 2.5MB quantized) to classify user intent in <30ms on CPU with three-way classification:

• Allow — On-topic healthcare query, pass to the LLM
• Deny — Off-topic, block with a polite redirect
• Abstain — Ambiguous, escalate to secondary classifier or human review

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Performance

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Model Details

Core Topics (Allow)

Symptoms, diagnosis, treatment, medications, preventive care, nutrition, mental health, fitness, chronic conditions, surgery, emergency care, health insurance, patient rights, telemedicine

Hard Exclusions (Deny)

Sports, entertainment, cooking, gaming, celebrity gossip, fashion, travel/leisure, fiction writing, relationship advice

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Usage

Python (ONNX Runtime)

import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np

tokenizer = AutoTokenizer.from_pretrained("perfecXion/intentguard-healthcare")
session = ort.InferenceSession("model.onnx")

text = "What are the symptoms of Type 2 diabetes?"
inputs = tokenizer(text, return_tensors="np", max_length=128, truncation=True, padding="max_length")

logits = session.run(None, {
    "input_ids": inputs["input_ids"],
    "attention_mask": inputs["attention_mask"]
})[0]

labels = ["allow", "deny", "abstain"]
prediction = labels[np.argmax(logits)]
confidence = float(np.max(np.exp(logits) / np.sum(np.exp(logits))))

print(f"Intent: {prediction} (confidence: {confidence:.3f})")
# Output: Intent: allow (confidence: 0.997)

Docker

docker pull ghcr.io/perfecxion/intentguard:healthcare-1.0
docker run -p 8080:8080 ghcr.io/perfecxion/intentguard:healthcare-1.0

curl -X POST http://localhost:8080/v1/classify \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "What medications interact with metformin?"}]}'

pip

pip install intentguard

from intentguard import IntentGuard
guard = IntentGuard.load("healthcare")
result = guard.classify("What are the symptoms of Type 2 diabetes?")
print(result)  # Intent(label='allow', confidence=0.997)

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Example Classifications

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Citation

@misc{thornton2025intentguard,
  title={IntentGuard: A Production-Grade Vertical Intent Classifier for LLM Guardrails},
  author={Thornton, Scott},
  year={2025},
  publisher={perfecXion.ai},
  url={https://perfecxion.ai/articles/intentguard-vertical-intent-classifier-llm-guardrails.html},
  note={Model: https://huggingface.co/perfecXion/intentguard-healthcare}
}

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Quality Metrics

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License

Apache 2.0

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Links

• Research Article: IntentGuard: A Production-Grade Vertical Intent Classifier
• Publisher: perfecXion.ai
• Finance Model: perfecXion/intentguard-finance
• Legal Model: perfecXion/intentguard-legal
• Docker Image: ghcr.io/perfecxion/intentguard:healthcare-1.0