| --- |
| 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} |
| } |
| ``` |
|
|
