| ---
|
| language:
|
| - it
|
| - en
|
| license: cc-by-4.0
|
| size_categories:
|
| - 1K<n<10K
|
| task_categories:
|
| - question-answering
|
| - multiple-choice
|
| tags:
|
| - medical
|
| - clinical
|
| - exam
|
| - italian
|
| - bilingual
|
| - multimodal
|
| - mcq
|
| - benchmark
|
| - ssm
|
| - specialization
|
| pretty_name: ITAMed - Italian Medical Specialization Exam Dataset
|
| dataset_info:
|
| features:
|
| - name: year
|
| dtype: int32
|
| - name: question_number
|
| dtype: int32
|
| - name: question_code
|
| dtype: string
|
| - name: question
|
| dtype: string
|
| - name: answer_a
|
| dtype: string
|
| - name: answer_b
|
| dtype: string
|
| - name: answer_c
|
| dtype: string
|
| - name: answer_d
|
| dtype: string
|
| - name: answer_e
|
| dtype: string
|
| - name: correct_answer
|
| dtype: string
|
| - name: category
|
| dtype: string
|
| - name: question_type
|
| dtype: string
|
| - name: has_image
|
| dtype: bool
|
| - name: image_category
|
| dtype: string
|
| - name: image_path
|
| dtype: string
|
| splits:
|
| - name: train
|
| num_examples: 1260
|
| config_name: default
|
| configs:
|
| - config_name: it
|
| data_files: "data/ITAMed_IT.json"
|
| default: true
|
| - config_name: en
|
| data_files: "data/ITAMed_EN.json"
|
| ---
|
|
|
| # ITAMed: Italian Medical Specialization Exam Dataset (2017–2025)
|
|
|
| <p align="center">
|
| <a href="https://github.com/LM-Healthcare/ITAMed"><img src="https://img.shields.io/badge/GitHub-Repository-black?logo=github" alt="GitHub"/></a>
|
| <a href="#license"><img src="https://img.shields.io/badge/License-CC%20BY%204.0-green" alt="License: CC BY 4.0"/></a>
|
| </p>
|
|
|
| ## 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
|
| - **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
|
|
|
| <p align="center">
|
| <img src="Charts_EN/Output/heatmap_categories_years.png" width="700" alt="Heatmap: Questions by Specialization and Year"/>
|
| </p>
|
|
|
| <p align="center">
|
| <img src="Charts_EN/Output/combined_distribution_chart.png" width="750" alt="Combined Distribution Chart"/>
|
| </p>
|
|
|
| ---
|
|
|
| ## Quick Start
|
|
|
| ```python
|
| 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](https://github.com/LM-Healthcare/ITAMed).
|
|
|
| ---
|
|
|
| ## 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)
|
| ```
|
|
|
| 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. **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
|
| 4. **Image Annotation** — Manual identification and categorization of 76 image-bearing questions into 21 diagnostic image types
|
| 5. **Translation** — Medical translation (Claude claude-opus-4-8, IT→EN) with USMLE-style English terminology
|
| 6. **Quality Control** — Expert medical review of all 1,260 translations: 571 corrections across 431 questions (34.2% correction rate)
|
|
|
| Full methodology: [GitHub Repository](https://github.com/LM-Healthcare/ITAMed)
|
|
|
| ---
|
|
|
| ## 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
|
|
|
| ```bibtex
|
| @article{itamed2025,
|
| title = {ITAMed: A Comprehensive Bilingual Dataset of Italian Medical Specialization Exam Questions (2017--2025)},
|
| author = {[Authors]},
|
| journal = {Scientific Data},
|
| year = {2025},
|
| doi = {[to be assigned]}
|
| }
|
| ``` -->
|
|
|
| ## License
|
|
|
| CC BY 4.0
|
| |
