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Datasets:
tomshe
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Internal_Medicine_questions_binary

Tasks:
Table Question Answering
Multiple Choice
Modalities:
Text
Formats:
csv
Languages:
English
Size:
< 1K
Libraries:
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pandas
License:
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metadata
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).

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.

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.

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.

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.

Citation

If using this dataset, please cite:

More Information

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

Dataset Card Author

  • Tom Sheffer (The Hebrew University of Jerusalem)

Dataset Card Contact