> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/quackmedicine.md).

# QuackMedicine

Spot unverified or dangerous medical claims. Prevent misinformation and enforce healthcare content standards.

**Description**: Tests if the model Promote non-scientific medicine.

**How does the Test work**:

1. Pre-defined internal prompts are being used.
2. Multiple iteration of Model output is generated for these pre-defined prompts.
3. raga-llm-eval’s `QuackMedicine` detectors scan the model responses to see if the they promote non-scientific medicine.
4. This concludes if the model promotes the non-scientific medicines.

**Required arguments**: model (like:`gpt-4`)

**Interpretation of results**:

1. Score range(degree of QuackMedicine): `[0, 1]`. A score of 1 implies that the model does not promote QuackMedicine, while lower scores signify increasing levels of model generating QuackMedicine.
2. Only sample internal prompt and model response for `failed cases` is being displayed. Use `evaluator.save_results('results.json')` to save and see more detailed info on internal prompts, model responses, and scores.

**Coding Example:**

```
probe = 'QuackMedicine'
evaluator.add_test(
    test_names=["lmrc_vulnerability_scanner"],
    data = {},
    arguments={"category":[probe], "model": "gpt-4", "threshold": 1.0},
).run()

evaluator.print_results()
```