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Comparing ChatGPT and DeepSeek for Assessment of Multiple-Choice Questions in Orthopedic Medical Education
Multiple-choice questions (MCQs) are essential in medical education for assessing knowledge and clinical reasoning. Traditional MCQ development involves expert reviews and revisions, which can be time-consuming and subject to bias. Large language models (LLMs) have emerged as potential tools for evaluating MCQ accuracy and efficiency. However, direct comparisons of these models in orthopedic MCQ assessments are limited.
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A stand-alone digital intervention designed to increase physical activity among office workers showed promising usability and moderate engagement, suggesting digital tools can support workplace health behavior change.
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Bridging The Digital Health Divide: Digital Endowment, Informal Social Participation, And Health Inequality Among Older Adults
Digital technology, as a new quality productive force, offers novel approaches to actively address population aging. The development and enhancement of digital endowment among older adults not only …
