ITAMed is a comprehensive, bilingual dataset of 1,260 multiple-choice medical questions from the Italian National Medical Specialization Entrance Exam (Concorso SSM — Scuole di Specializzazione in Medicina), spanning 9 consecutive years (2017–2025).

Every question has been classified into one of 28 medical specialties through a rigorous dual-annotator LLM protocol validated by independent expert adjudication, and professionally translated from Italian to English with expert review.

Key Features

1,260 questions — 140 per year across 9 years (2017–2025)
Bilingual — Original Italian + expert-reviewed English translation
28 medical specialties — Dual-LLM classification (κ=0.895) with expert adjudication
Question-type annotation — 928 case-based (73.7%) and 332 knowledge-based (26.3%), classified by two medical specialists
76 image-bearing questions — With 21 diagnostic image-type categories
Ready for benchmarking — Standard MCQ format with shuffled-answer utility for unbiased evaluation

Specialty Distribution

Heatmap: Questions by Specialization and Year

Combined Distribution Chart

Quick Start

from datasets import load_dataset

# Load Italian version (default)
dataset = load_dataset("Filo-White/ITAMed", "it")

# Load English version
dataset_en = load_dataset("Filo-White/ITAMed", "en")

# Filter by specialty
cardiology = dataset["train"].filter(lambda x: "Cardiology" in x["category"])

# Filter by year
year_2024 = dataset["train"].filter(lambda x: x["year"] == 2024)

# Image-bearing questions only
with_images = dataset["train"].filter(lambda x: x["has_image"])

Available Files

Path	Format	Description
data/ITAMed_IT.json	JSON	Complete Italian dataset (1,260 questions)
data/ITAMed_EN.json	JSON	Complete English dataset (1,260 questions)
images/{year}/	PNG/JPG	Diagnostic images referenced by questions (76 total)

The canonical repository with per-year JSON/XLSX files, distribution workbooks, and full documentation is hosted on GitHub.

Dataset Schema

Field	Type	Description
`year`	`int`	Exam year (2017–2025)
`question_number`	`int`	Position within the year (1–140)
`question_code`	`string`	Official unique question identifier
`question`	`string`	Full question text (clinical vignette + stem)
`answer_a`	`string`	Answer option A (always correct)
`answer_b` – `answer_e`	`string`	Distractor options
`correct_answer`	`string`	Always `"A"`
`category`	`string`	Medical specialty (1–2, semicolon-separated)
`question_type`	`string`	`case-based` or `knowledge-based`
`has_image`	`bool`	Whether the question references an image
`image_category`	`string`	Type of diagnostic image (21 categories)
`image_path`	`string`	Relative path to image file

Statistics

Metric	Value
Total questions	1,260
Years covered	2017–2025 (9 years)
Questions per year	140
Case-based questions	928 (73.7%)
Knowledge-based questions	332 (26.3%)
Single-specialty questions	824 (65.4%)
Multi-specialty questions	436 (34.6%)
Questions with images	76 (6.0%)
Medical specialties	28
Image categories	21
Languages	Italian + English

Top 10 Specialties (by question count)

#	Specialty	Count
1	Cardiology and Cardiac Surgery	154
2	Oncology	112
3	Pulmonology and Thoracic Surgery	103
4	Neurology and Neurosurgery	100
5	Pediatrics	95
6	Gastroenterology	93
7	Gynecology and Obstetrics	87
8	Infectious Diseases	84
9	General Surgery	80
10	Endocrinology	76

Yearly Image Distribution

Year	2017	2018	2019	2020	2021	2022	2023	2024	2025
Images	14	9	5	6	6	4	11	8	13

Construction Pipeline

PDF Sources (MUR)  →  Text Extraction  →  Classification  →  Translation  →  Quality Control
   (9 PDFs)           (pdfplumber)        (dual-LLM +         (Claude +       (expert review
                                           expert review)      expert review)   + validation)

PDF Extraction — Automated text extraction with pdfplumber and custom regex parsers, followed by manual verification against source documents
Specialty Classification — Dual-annotator LLM protocol:
- Independent classification by Claude claude-opus-4-8 and GPT-5.5 (Cohen's κ = 0.895, "almost perfect")
- 380 disagreements reviewed by two independent medical specialists (inter-reviewer κ = 0.856)
- 126 remaining discordances resolved through expert consensus
Question-Type Annotation — 928 case-based and 332 knowledge-based, jointly classified by two medical specialists following operational criteria distinguishing clinical reasoning from factual recall
Image Annotation — Manual identification and categorization of 76 image-bearing questions into 21 diagnostic image types
Translation — Medical translation (Claude claude-opus-4-8, IT→EN) with USMLE-style English terminology
Quality Control — Expert medical review of all 1,260 translations: 571 corrections across 431 questions (34.2% correction rate)

Full methodology: GitHub Repository

Data Source

Questions were extracted from the official PDF documents of the Italian National Medical Specialization Entrance Exam (Concorso per l'ammissione alle Scuole di Specializzazione in Medicina e Chirurgia), administered annually by the Italian Ministry of University and Research (MUR).

2017–2019: Scenario-based format (questions grouped under shared clinical scenarios)
2020–2025: Standalone format (self-contained questions)
Correct answer: Always option A (official release format)

Use Cases

LLM Medical Benchmarking — Evaluate language models on real clinical exam questions in Italian and/or English
Cross-lingual Medical NLP — Compare model performance across Italian and English on identical clinical content
Medical Education Research — Analyze question difficulty, topic distribution, and temporal trends
Multimodal Medical AI — Evaluate vision-language models on image-bearing clinical questions

Limitations

The correct answer is always in position A. A shuffle_answers.py utility (seed-based, deterministic) is provided for unbiased evaluation.
Image-bearing questions (6%) require the associated image for complete understanding.
Translation was LLM-generated and expert-reviewed; some nuances may differ from native human medical translation.

Citation

If you use ITAMed or the associated code in your research, please cite the Zenodo dataset record. We also encourage you to cite the accompanying manuscript once published in Scientific Data.

Dataset (Zenodo)

Bianchini, F., Bianchini, E., Bianchini, E., Marano, M., Mecella, M., & VUILLERME, N. (2026). A bilingual Italian–English dataset of case-based and knowledge-based questions from medical specialization examinations [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.21824685

Paper (Under Review)

The accompanying manuscript is currently under review at Scientific Data.

Bianchini, F., Bianchini, E., Bianchini, E., Marano, M., Mecella, M., & Vuillerme, N. (2026).
A bilingual Italian and English dataset of clinical case questions from medical specialization exams.
Manuscript submitted for publication to Scientific Data.

BibTeX

@dataset{bianchini_2026_21824685,
  author       = {Bianchini, Filippo and
                  Bianchini, Edoardo and
                  Bianchini, Emiliano and
                  Marano, Massimo and
                  Mecella, Massimo and
                  VUILLERME, Nicolas},
  title        = {A bilingual Italian–English dataset of case-based
                   and knowledge-based questions from medical
                   specialization examinations
                  },
  month        = aug,
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.21824685},
  url          = {https://doi.org/10.5281/zenodo.21824685},
}

License

CC BY 4.0

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