---
license: cc-by-4.0
pretty_name: Clinical AI Benchmark Atlas (CABA)
language:
  - en
size_categories:
  - 10K<n<100K
tags:
  - medical
  - clinical-ai
  - benchmarks
  - llm-evaluation
configs:
  - config_name: registry
    data_files: registry.csv
  - config_name: per_benchmark
    data_files: per_benchmark.csv
  - config_name: labels
    data_files: labels.csv
---

# Clinical AI Benchmark Atlas (CABA)

CABA is a registry of the public benchmarks used to evaluate large language models in health care: the 58
benchmarks named by 13 evaluation suites and by frontier model reports, checked against five criteria on their
downloaded items. Up to 200 items from each carry [RCQ-Map](https://github.com/nyuolab/RCQ-Map) labels, and
every benchmark is compared with the task mix of real clinical queries that clinicians sent to an institutional
AI assistant. Version 1.0 is the version described in the paper.

**Benchmark items are not included.** Each benchmark keeps its own license, and some ask that their items not be
posted online. CABA releases item ids and labels; the scripts in the [GitHub repository](https://github.com/nyuolab/CABA)
rebuild the items from each benchmark's own release.

## Files

| Config | Rows | Contents |
|---|---|---|
| `registry` | 58 | One row per benchmark: evaluation suites, frontier model reports, items sampled and labeled, split, version, license, source, rendering rule |
| `per_benchmark` | 58 | Task-category and kind-of-work shares per benchmark, and overlap with the real clinical task mix |
| `labels` | 10,659 | One row per benchmark item: `item_id`, `benchmark` and the 24 RCQ-Map fields. No item text |

`rcq_aggregate.json` holds the aggregate shares of the 127,625 real clinical queries used for comparison.

```python
from datasets import load_dataset
labels = load_dataset("NYU-OLAB/CABA", "labels", split="train")
```

## How the labels were made

Up to 200 items per benchmark were sampled, rendered as a model receives them, and labeled with RCQ-Map by the
paper's annotator (GPT-5.6 sol, reasoning effort none, the paper's guidelines, strict JSON schema). The 40 sampled
Med-HALT items that failed the schema's consistency rules are listed in the GitHub repository. The protocol, the
registry build stages and the comparison script are there as well, and `scripts/compare.py` reproduces the paper's
medians from these labels.

## License and citation

Registry, labels, summaries and reference shares: CC BY 4.0. The benchmarks themselves keep their own licenses,
recorded in `registry`. If you use CABA, please cite the paper (Vishwanath et al., 2026; see the GitHub
repository's `CITATION.cff`).