MRareBench: A Multimodal Rare-Disease Benchmark for Evidence–Diagnosis Correspondence

Benchmark data accompanying the paper, released for review.

Items are built from open-access PubMed Central case reports. Imaging findings are withheld from the question context, so a model that ignores the image cannot recover the answer from the text alone.

Files

File	Task	Items
diagnosis/diagnosis_opened.tsv	T1 — forward diagnosis, ranked differential	300
evidence_verification/evidence_verification_opened_FULL609.tsv	T2 — evidence verification, open-ended	608

Images are embedded in the image column as a JSON list of base64 strings, so the files are self-contained.

Reading the files

Use a CSV parser with quoting enabled. Do not split on newlines: most records contain newline characters inside their text fields, so the physical line count far exceeds the record count.

import pandas as pd
t1 = pd.read_csv('diagnosis/diagnosis_opened.tsv', sep='\t', dtype=str)   # 300 rows
t2 = pd.read_csv('evidence_verification/evidence_verification_opened_FULL609.tsv',
                 sep='\t', dtype=str)                                     # 608 rows

Notes

Not for clinical use. A score here is not evidence of clinical safety or diagnostic validity.

Source articles are open access and carry their own per-article licenses.