ITAMed: Italian Medical Specialization Exam Dataset (2017–2025)

Dataset Description

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
• 76 image-bearing questions — With 21 diagnostic image-type categories
• Ready for benchmarking — Standard MCQ format compatible with LLM evaluation frameworks

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Specialty Distribution

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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"])

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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)
data/xlsx/IT/ITAMed_{year}.xlsx	XLSX	Italian per-year files (2017–2025)
data/xlsx/IT/ITAMed_complete.xlsx	XLSX	Italian complete file
data/xlsx/EN/ITAMed_{year}_EN.xlsx	XLSX	English per-year files (2017–2025)
data/xlsx/EN/ITAMed_complete_EN.xlsx	XLSX	English complete file

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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)
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

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Statistics

Metric	Value
Total questions	1,260
Years covered	2017–2025 (9 years)
Questions per year	140
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

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Construction Pipeline

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

1. PDF Extraction — Automated text extraction with pdfplumber and custom regex parsers, followed by manual verification against source documents
2. 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
3. Image Annotation — Manual identification and categorization of 76 image-bearing questions into 21 diagnostic image types
4. Translation — Medical translation (Claude claude-opus-4-8, IT→EN) with USMLE-style English terminology
5. Quality Control — Expert medical review of all 1,260 translations: 571 corrections across 431 questions (34.2% correction rate)

Full methodology: GitHub Repository

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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)

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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

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Limitations

• The correct answer is always in position A. For benchmarking, answer positions should be shuffled before evaluation to prevent position bias.
• 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.

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License

CC BY 4.0