Generation of Medical Case-Based Multiple-Choice Questions

Abstract

This narrative review is a detailed look at how we make multiple-choice questions (MCQs) based on medical cases in today’s medical teaching. Moving from old-style MCQs to ones that are more related to real clinical situations is really important. It helps in growing critical thinking and practical use, especially since MCQs are still the primary method for testing knowledge in medicine. We look at the history, design ideas, and both manual and computer-based methods that have helped create MCQs. Technologies like Artificial Intelligence (AI) and Natural Language Processing (NLP) are receiving a lot of focus for their ability to automate the creation of question. We also talk about the challenges of using real patient cases, like the need for exact clinical information, reducing unclear information, and thinking about ethical issues. We also investigate the measures of validity and reliability that are crucial to maintaining the honesty of case-based MCQs. Finally, we look ahead, speculating on where medical education is headed as new technologies are incorporated and the value of case-based evaluations continues to rise.

Keywords

• medical case-based multiple-choice questions (CB-MCQs)
• distractors
• artificial intelligence (AI)
• natural language processing (NLP)