---
license: cc-by-4.0
task_categories:
- table-question-answering
- multiple-choice
language:
- en
pretty_name: Internal Medicine MCQ
size_categories:
- n<1K
---

# Dataset Card for **Internal Medicine MCQ**


## Dataset Details

### **Dataset Description**

This dataset consists of **41 high-quality**, two-choice multiple-choice questions (MCQs) focused on **core biomedical knowledge** and clinical scenarios from **internal medicine**. These questions were specifically curated for research evaluating medical knowledge, clinical reasoning, and confidence-based interactions among medical trainees and large language models (LLMs).

* **Curated by:** Tom Sheffer
* **Shared by:** Tom Sheffer (The Hebrew University of Jerusalem)
* **Language:** English
* **License:** [Creative Commons Attribution 4.0 International (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
* **Paper:** *\[Information Needed]*

---

## Uses

### **Direct Use**

This dataset is suitable for:

* Evaluating medical knowledge and clinical reasoning skills of medical students and healthcare professionals.
* Benchmarking performance and reasoning capabilities of large language models (LLMs) in medical question-answering tasks.
* Research on collaborative human–AI and human–human interactions involving clinical decision-making.

### **Out-of-Scope Use**

* **Not intended** as a diagnostic or clinical decision-making tool in real clinical settings.
* Should **not** be used to train systems intended for direct clinical application without extensive validation.

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## Dataset Structure

The dataset comprises **41 multiple-choice questions** with two answer choices (binary-choice format). The dataset includes the following fields:

* `question_id`: A unique identifier for each question.
* `question_text`: The clinical vignette or biomedical question.
* `optionA`: First possible answer choice.
* `optionB`: Second possible answer choice.
* `answer`: The correct answer text.
* `answer_idx`: The correct answer choice (A or B).

---

## Dataset Creation

### **Curation Rationale**

The dataset was created to study **knowledge diversity**, internal confidence, and collaborative decision-making between medical trainees and AI agents. Questions were carefully selected to represent authentic licensing exam–style questions in internal medicine, ensuring ecological validity for medical education and AI–human collaborative studies.

---

### **Source Data**

#### **Data Collection and Processing**

The questions were sourced and adapted from standardized medical licensing preparation materials. All questions were reviewed, translated, and validated by licensed physicians.

#### **Who are the source data producers?**

The original data sources are standard medical licensing examination preparation materials.

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### **Personal and Sensitive Information**

The dataset **does not contain** any personal, sensitive, or identifiable patient or clinician information. All clinical scenarios are fictionalized or generalized for educational and research purposes.

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## Bias, Risks, and Limitations

* The dataset size (**41 questions**) is limited; therefore, findings using this dataset might not generalize broadly.
* Content is limited to internal medicine; results may not generalize across all medical specialties.

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## Citation

If using this dataset, please cite:

```bibtex

```

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## More Information

For more details, please contact the dataset author listed below.

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## Dataset Card Author

* **Tom Sheffer** (The Hebrew University of Jerusalem)

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## Dataset Card Contact

* **Email:** [sheffer.sheffer@gmail.com](mailto:sheffer.sheffer@gmail.com)

---