# Medhal Eval

> Evaluates AI models' ability to detect factual inconsistencies (hallucinations) in medical text and generate grounded explanations for why statements are non-factual. It probes domain-specific factual consistency reasoning and binary classification under clinical constraints. Use when the user wants to benchmark on MedHal, MedNLI, Hegselmann et al. (2024a) Hallucination Dataset, or asks about evaluating this task. Reports F1-score.

- Skill: `qhjqhj00/medhal-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/medhal-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/medhal-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/medhal-eval

---


# medhal-eval

> MedHal: An Evaluation Dataset for Medical Hallucination Detection — Mehenni et al. (2025) (arXiv:2504.08596, 2025)

## What this evaluates

Evaluates AI models' ability to detect factual inconsistencies (hallucinations) in medical text and generate grounded explanations for why statements are non-factual. It probes domain-specific factual consistency reasoning and binary classification under clinical constraints.

## Datasets

- **MedHal** — total ?; splits: train (-1), test (-1)
- **MedNLI** — total ?; splits: test (-1)
- **Hegselmann et al. (2024a) Hallucination Dataset** — total 210; splits: test (210)

## Metrics

- `F1-score` **(primary)** — range: [0, 1]
  - Harmonic mean of precision and recall: 2 * (P * R) / (P + R). Measures overall accuracy in distinguishing factual from non-factual medical statements.
- `Precision, Recall` — range: [0, 1]
  - Precision: TP / (TP + FP). Recall: TP / (TP + FN). Standard binary classification metrics for factuality detection.
- `ROUGE-1, ROUGE-2, BLEU` — range: [0, 1]
  - Standard n-gram overlap metrics (Lin 2004; Papineni et al. 2002) comparing generated explanations against ground truth. Only computed when both model prediction and gold label are non-factual.

## Input / output format

**Input**: Medical text statements (from clinical notes, trials, QA, NLI, summarization, or IE tasks) provided within a prompt template asking the model to classify factual consistency and explain non-factual content.

**Output**: Binary classification label (factual or non-factual) followed by a textual explanation pinpointing the exact erroneous part of the statement if labeled non-factual.

## Scoring recipe

```python
def compute_metrics(preds, gold, pred_exps, gold_exps):
    tp = sum(1 for p, g in zip(preds, gold) if p == 'non-factual' and g == 'non-factual')
    fp = sum(1 for p, g in zip(preds, gold) if p == 'non-factual' and g == 'factual')
    fn = sum(1 for p, g in zip(preds, gold) if p == 'factual' and g == 'non-factual')
    prec = tp / (tp + fp) if (tp + fp) > 0 else 0
    rec = tp / (tp + fn) if (tp + fn) > 0 else 0
    f1 = 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
    
    valid_idx = [i for i, (p, g) in enumerate(zip(preds, gold)) if p == 'non-factual' and g == 'non-factual']
    r1 = rouge_score([gold_exps[i] for i in valid_idx], [pred_exps[i] for i in valid_idx], rouge_types=['rouge1'])
    r2 = rouge_score([gold_exps[i] for i in valid_idx], [pred_exps[i] for i in valid_idx], rouge_types=['rouge2'])
    bleu = bleu_score([gold_exps[i] for i in valid_idx], [pred_exps[i] for i in valid_idx])
    return f1, prec, rec, r1, r2, bleu
```

## Common pitfalls

- Explanation metrics (ROUGE/BLEU) are only valid when both the model's prediction and the ground truth label indicate a non-factual statement; computing them on factual samples or mismatched predictions invalidates the metric.
- Downstream evaluation on MedNLI requires filtering out neutral-labeled samples, as the MedHal-trained models are not designed to detect neutral statements.
- Zero-shot prompting consistently fails on the Hegselmann et al. hallucination dataset; a 1-shot strategy is required for fair comparison.

## Evidence (verbatim from paper)

> We use two main types of metrics to evaluate the models: factuality metrics and explanation metrics. Factuality metrics measure how accurately a model identifies factual versus non-factual content. This includes common measures like precision, recall, and F1-score. Due to inconsistencies in model output when using the prompt format from Figure [5]. You can see these results in Table [6]. Explanation metrics assess the validity of the explanations that the models provide for non-factual statements. Specifically, these metrics check if a model, after identifying non-factual content, correctly pinpoints the exact erroneous part of the statement. The explanation metrics are ROUGE-1 (R1), ROUGE-2 (R2) *Lin ([2004])*, and BLEU *Papineni et al. ([2002])* scores. To ensure a valid comparison, we only consider samples where both the model’s prediction and the ground truth label indicate a non-factual statement. This guarantees that a true explanation exists and that the model attempted to generate one.

## Citation

```bibtex
@misc{mehenni2025medhal,
  title={MedHal: An Evaluation Dataset for Medical Hallucination Detection},
  author={Mehenni et al. (2025)},
  year={2025},
  note={arXiv:2504.08596}
}
```

- arXiv: 2504.08596