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license: cc-by-4.0 |
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task_categories: |
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- table-question-answering |
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- multiple-choice |
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language: |
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- en |
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pretty_name: Internal Medicine MCQ |
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size_categories: |
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- n<1K |
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--- |
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# Dataset Card for **Internal Medicine MCQ** |
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## Dataset Details |
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### **Dataset Description** |
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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). |
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* **Curated by:** Tom Sheffer |
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* **Shared by:** Tom Sheffer (The Hebrew University of Jerusalem) |
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* **Language:** English |
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* **License:** [Creative Commons Attribution 4.0 International (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/) |
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* **Paper:** *\[Information Needed]* |
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--- |
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## Uses |
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### **Direct Use** |
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This dataset is suitable for: |
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* Evaluating medical knowledge and clinical reasoning skills of medical students and healthcare professionals. |
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* Benchmarking performance and reasoning capabilities of large language models (LLMs) in medical question-answering tasks. |
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* Research on collaborative human–AI and human–human interactions involving clinical decision-making. |
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### **Out-of-Scope Use** |
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* **Not intended** as a diagnostic or clinical decision-making tool in real clinical settings. |
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* Should **not** be used to train systems intended for direct clinical application without extensive validation. |
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--- |
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## Dataset Structure |
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The dataset comprises **41 multiple-choice questions** with two answer choices (binary-choice format). The dataset includes the following fields: |
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* `question_id`: A unique identifier for each question. |
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* `question_text`: The clinical vignette or biomedical question. |
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* `optionA`: First possible answer choice. |
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* `optionB`: Second possible answer choice. |
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* `answer`: The correct answer text. |
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* `answer_idx`: The correct answer choice (A or B). |
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--- |
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## Dataset Creation |
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### **Curation Rationale** |
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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. |
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### **Source Data** |
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#### **Data Collection and Processing** |
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The questions were sourced and adapted from standardized medical licensing preparation materials. All questions were reviewed, translated, and validated by licensed physicians. |
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#### **Who are the source data producers?** |
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The original data sources are standard medical licensing examination preparation materials. |
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### **Personal and Sensitive Information** |
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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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--- |
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## Bias, Risks, and Limitations |
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* The dataset size (**41 questions**) is limited; therefore, findings using this dataset might not generalize broadly. |
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* Content is limited to internal medicine; results may not generalize across all medical specialties. |
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--- |
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## Citation |
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If using this dataset, please cite: |
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```bibtex |
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``` |
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--- |
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## More Information |
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For more details, please contact the dataset author listed below. |
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## Dataset Card Author |
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* **Tom Sheffer** (The Hebrew University of Jerusalem) |
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## Dataset Card Contact |
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* **Email:** [[email protected]](mailto:[email protected]) |
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--- |
