| # 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 benchmarks contains 50 |
| cases. |
|
|
| The public train split (250 cases) is in this repository and on |
| [Hugging Face](https://huggingface.co/datasets/doximity/bedside-bench). |
| The held-out test split is not published. |
|
|
| > **Not for clinical use.** BedsideBench evaluates AI systems. It is not medical |
| > advice and must not be used for patient care. See [DISCLAIMER.md](DISCLAIMER.md). |
|
|
| ## Benchmarks |
|
|
| | Benchmark | Grading | Scope | |
| |---|---|---| |
| | `drug_safety` | points | Drug interactions, contraindications, dosing, and cross-reactivity | |
| | `guideline_adherence` | points | Guideline-concordant care | |
| | `landmark_trials` | points | Trial evidence and outcome interpretation | |
| | `medical_hallucination` | points | Fictitious entities and false premises | |
| | `health_equity` | points | Bias-aware reasoning and barriers to care | |
| | `clinical_calculations` | points | Doses, scores, and unit conversions | |
| | `calculators_numerical` | points | Medical calculators with numeric outputs | |
| | `calculators_conditional` | points | Medical calculators with categorical or conditional outputs | |
| | `safety_diagnostic` | f1_weighted_rubric | Diagnostic workup, referral, procedures, and follow-up | |
| | `safety_therapeutic` | f1_weighted_rubric | Medication, counseling, and treatment plans | |
|
|
| ## Load the data |
|
|
| ```python |
| from datasets import load_dataset |
| |
| all_cases = load_dataset("doximity/bedside-bench", "all", split="train") |
| drug_safety = load_dataset( |
| "doximity/bedside-bench", |
| "drug_safety", |
| split="train", |
| ) |
| ``` |
|
|
| Each case contains: |
|
|
| - `case_id`: stable identifier; |
| - `benchmark`: one of the 10 benchmark names above; |
| - `grading_method`: `points_rubric` or `f1_weighted_rubric`; |
| - `prompt`: question sent to the model; |
| - `rubric_items`: criteria with `item_id`, `text`, and signed `points`. |
| `item_id` is `{md5(prompt)}_{index}` so it is unique across the panel. |
|
|
| Positive rubric items describe content that should appear in an answer. Negative |
| items describe errors or harmful actions. On points rubrics, zero-weight items |
| are retained for analysis but do not affect the score. On |
| `f1_weighted_rubric`, a matched equivocal (`points=0`) item enters the |
| precision denominator. |
|
|
| ## Grade a model |
|
|
| [`docs/grading_protocol.md`](docs/grading_protocol.md) contains the judge prompt, |
| case-level formulas, aggregation rules, and confidence-interval procedure. |
| Safety cases use `f1_weighted_rubric` scoring (`bedside-bench-grading-v1`): |
| signed `points` indicating a severity weight, and the case score is weighted F1. |
|
|
| ```text |
| points +3 +2 +1 0 -1 -2 -3 |
| weight 72 24 3 1 3 24 72 |
| |
| R = Σ w matched, class ≥ 7 / Σ w all, class ≥ 7 |
| P = Σ w matched, class ≥ 7 / Σ w all matched |
| F1 = 2PR / (P + R), or 0 when P + R = 0 |
| ``` |
|
|
| This follows the published NOHARM F1 protocol (Wu et al., |
| [arXiv:2512.01241](https://arxiv.org/abs/2512.01241)), whose mild/moderate/severe |
| weights of 1/8/24 and equivocal 1/3 are ours scaled by 3; the factor cancels in |
| both ratios. `severe_rate` is reported per case as a diagnostic only. |
| [`evaluation/run_benchmark.py`](evaluation/run_benchmark.py) implements that |
| protocol and checkpoints every completed case. |
|
|
| Set `OPENAI_ACCESS_TOKEN` or `OPENAI_API_KEY`, then run one case from each |
| benchmark: |
|
|
| ```bash |
| python3 evaluation/run_benchmark.py \ |
| --answer-model gpt-5.4-mini \ |
| --judge-model gpt-5.6-sol \ |
| --output tmp/evaluations/gpt-5.4-mini-smoke.jsonl \ |
| --smoke |
| ``` |
|
|
| Remove `--smoke` and use a new output path to grade every case present in |
| this checkout. A public clone has the 250-case train split. |
|
|
| ## Repository structure |
|
|
| ```text |
| data/{benchmark}/ # manifests and case JSON |
| evaluation/ # reference grading runner and tests |
| schemas/ # JSON Schemas |
| scripts/ # validation and loading utilities |
| registry.json # benchmark registry |
| cluster_plan.json # benchmark grouping metadata |
| ``` |
|
|
| ## Validate |
|
|
| Requires Python 3.10+ and the packages in `scripts/requirements.txt`. |
|
|
| ```bash |
| python3 scripts/validate_registry.py |
| python3 scripts/validate_cases.py |
| python3 -m unittest discover -s evaluation -p "test_*.py" |
| ``` |
|
|
| ## Release details |
|
|
| - License: [CC BY-NC-SA 4.0](LICENSE) |
| - Citation metadata: [CITATION.cff](CITATION.cff) |
| - Safety and use restrictions: [DISCLAIMER.md](DISCLAIMER.md) |
|
|
