Datasets:

tomshe
/

Internal_Medicine_questions_binary

@@ -1,93 +1,112 @@
 ---
-license: cc-by-4.0
-task_categories:
-- question-answering
-- multiple-choice
-language:
-- en
-pretty_name: Internal_Medicine
-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)
-Paper: [...]
-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
-This dataset is not intended as a diagnostic or clinical decision-making tool in real clinical settings.
-It 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):
-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:
-BibTeX:
-More Information
-For more details, please contact the dataset authors listed below.
-Dataset Card Authors
-Tom Sheffer (The Hebrew University of Jerusalem)
-Dataset Card Contact
-Tom Sheffer: [email protected]

+Here's your improved, styled, and formatted dataset card:
+---
+# 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.
+---
+## 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:
+```bibtex
+```
+---
+## 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
+* **Email:** [[email protected]](mailto:[email protected])
+---