README.md

---
license: cc-by-nc-sa-4.0
language:
  - en
task_categories:
  - question-answering
  - text-generation
tags:
  - medical
  - clinical
  - benchmark
  - llm-evaluation
  - rubric-grading
  - patient-safety
pretty_name: BedsideBench
size_categories:
  - n<1K
configs:
  - config_name: all
    default: true
    data_files:
      - split: test
        path: data/all/test.parquet
  - config_name: drug_safety
    data_files:
      - split: test
        path: data/drug_safety/test.parquet
  - config_name: guideline_adherence
    data_files:
      - split: test
        path: data/guideline_adherence/test.parquet
  - config_name: landmark_trials
    data_files:
      - split: test
        path: data/landmark_trials/test.parquet
  - config_name: medical_hallucination
    data_files:
      - split: test
        path: data/medical_hallucination/test.parquet
  - config_name: health_equity
    data_files:
      - split: test
        path: data/health_equity/test.parquet
  - config_name: clinical_calculations
    data_files:
      - split: test
        path: data/clinical_calculations/test.parquet
  - config_name: calculators_numerical
    data_files:
      - split: test
        path: data/calculators_numerical/test.parquet
  - config_name: calculators_conditional
    data_files:
      - split: test
        path: data/calculators_conditional/test.parquet
  - config_name: safety_diagnostic
    data_files:
      - split: test
        path: data/safety_diagnostic/test.parquet
  - config_name: safety_therapeutic
    data_files:
      - split: test
        path: data/safety_therapeutic/test.parquet
---

# BedsideBench

BedsideBench is a clinical question-answering benchmark with 500 cases and
public grading rubrics. It covers factual questions, calculations, false-premise
queries, and free-text management plans. Each of the 10 configs contains 50
cases; the default `all` config contains all 500.

> **Not for clinical use.** BedsideBench evaluates AI systems. It is not medical
> advice and must not be used for patient care.

## Configs

| Config | Grading | n | Description |
|--------|---------|--:|-------------|
| `drug_safety` | points | 50 | Drug interactions, contraindications, dosing, and cross-reactivity |
| `guideline_adherence` | points | 50 | Guideline-concordant care |
| `landmark_trials` | points | 50 | Trial evidence and outcome interpretation |
| `medical_hallucination` | points | 50 | Fictitious entities and false premises |
| `health_equity` | points | 50 | Bias-aware reasoning and barriers to care |
| `clinical_calculations` | points | 50 | Doses, scores, and unit conversions |
| `calculators_numerical` | points | 50 | Medical calculators with numeric outputs |
| `calculators_conditional` | points | 50 | Medical calculators with categorical or conditional outputs |
| `safety_diagnostic` | weighted F1 | 50 | Diagnostic workup, referral, procedures, and follow-up |
| `safety_therapeutic` | weighted F1 | 50 | Medication, counseling, and treatment plans |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("doximity/bedside-bench", "all", split="test")

drug_safety = load_dataset("doximity/bedside-bench", "drug_safety", split="test")

case = drug_safety[0]
print(case["prompt"])
for item in case["rubric_items"]:
    print(item["points"], item["text"])
```

## Schema

Every config uses the same fields:

| Field | Type | Description |
|-------|------|-------------|
| `case_id` | string | Stable case identifier |
| `benchmark` | string | Benchmark name |
| `grading_method` | string | `points_rubric` or `f1_weighted_rubric` |
| `prompt` | string | Question sent to the model |
| `rubric_items` | list | Grading criteria |

Each rubric item contains:

| Field | Type | Description |
|-------|------|-------------|
| `item_id` | string | Stable item identifier |
| `text` | string | Criterion or candidate action |
| `points` | float | Signed weight |

## Grading

An LLM judge makes one binary matched/unmatched decision for every rubric item.
It counts an item only when the answer explicitly states or clearly entails the
criterion. For a harmful item, a match means the answer endorses the action;
mentioning it only to reject it is not a match.

For `points_rubric`, matched positive criteria add their weight and matched
negative criteria subtract their weight. Divide by the available positive
weight, then clamp the result to `[0, 1]`.

For `f1_weighted_rubric`:

- weighted true positives are matched positive actions;
- weighted false negatives are unmatched positive actions;
- weighted false positives are matched negative actions, using absolute weight;
- zero-weight actions do not affect the score.

Compute precision and recall from those weighted totals, then their harmonic
mean. Each config score is the mean of its 50 case scores. The overall score is
the macro-average of the 10 config means.

Reference evaluation uses one answer per case, one judge pass, and percentile
95% bootstrap confidence intervals with 10,000 resamples and seed 42.

## Reference result

`gpt-5.4-mini` scored **0.711** (95% bootstrap CI 0.681–0.739) over
500/500 cases, judged once per answer by `gpt-5.6-sol`. Model identifiers do not
guarantee immutable provider behavior, so comparisons are strongest when models
are evaluated together under the same configuration.

## Construction and limitations

Cases were developed and validated for physician-led evaluation. This release
is a deterministic sample (seed 42) from a larger pool. Cases are synthetic or
de-identified constructions, not patient records.

Rubrics reflect clinical judgment and the available standard of care when they
were written. Guidelines change, LLM judges can make classification errors, and
public benchmark cases may enter future training data. A score does not
establish that a model is safe or suitable for clinical use.

Some cases deliberately contain false premises, fictitious entities, or unsafe
actions. Model responses may also be incorrect or unsafe.

## License

[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) — Attribution-NonCommercial-ShareAlike. © 2026 Doximity, Inc.

## Citation

```bibtex
@dataset{doximity_bedside_bench,
  title   = {BedsideBench: A Clinical Question-Answering Benchmark},
  author  = {{Doximity}},
  year    = {2026},
  version = {0.2.0},
  license = {CC-BY-NC-SA-4.0}
}
```