JMIR Medical Education

Academic and educational institutions are making significant contributions toward training health informatics professionals. As research in health informatics education (HIE) continues to grow, it is useful to have a clearer understanding of this research field.

Chat Generative Pre-training Transformer (ChatGPT) is an artificial intelligence natural language model developed by OpenAI. It generates new texts, responds to user inputs conversationally, and can summarize and translate text. In medical application, it has been evaluated for use in areas like answering NBME Step 1 questions, with over 60% accuracy, and supporting clinical practice and scientific writing. However, its potential for improving patient outcomes and addressing healthcare disparities has not been thoroughly investigated.

The General Medicine In-training Examination (GM-ITE) tests clinical knowledge in a two-year postgraduate residency program in Japan. In the academic year 2021, as a domain of medical safety, the GM-ITE included questions regarding the diagnosis from medical history and physical findings through video viewing and the skills in presenting a case. Examinees watched a video/audio recording of a patient examination and provided free-text responses. However, the human cost of scoring free-text answers may limit the implementation of GM-ITE. A simple morphological analysis and word-matching model, thus, can be used to score free-text responses.

ChatGPT, a recently developed AI chatbot and a notably large language model (LLMs), has demonstrated improved performance in medical field examinations. However, there is currently little research on its efficacy in languages other than English, or in pharmacy-related examinations.

The digitalization of health care organizations is an integral part of a clinician’s daily life, making it vital for health care professionals (HCPs) to understand and effectively use digital tools in hospital settings. However, clinicians often express a lack of preparedness for their digital work environments. Particularly, new clinical end users, encompassing medical and nursing students, seasoned professionals transitioning to new health care environments, and experienced practitioners encountering new health care technologies, face critically intense learning periods, often with a lack of adequate time for learning digital tools, resulting in difficulties in integrating and adopting these digital tools into clinical practice.

This study explores the cutting-edge abilities of large language models (LLMs) such as ChatGPT in medical history taking and medical chart documentation, with a focus on their practical effectiveness in clinical settings—an area vital for the progress of medical artificial intelligence.

Generative large language models (LLMs) have the potential to revolutionize medical education by generating tailored learning materials, enhancing teaching efficiency, and improving learner engagement. However, the application of LLMs in healthcare settings, particularly for augmenting small datasets in text classification tasks, remains underexplored, particularly for cost- and privacy-conscious applications that do not permit the use of third-party services such as OpenAI’s ChatGPT.

Since the release of ChatGPT in November 2022, this emerging technology has garnered a lot of attention in various fields, and nursing is no exception. However, to date, no study has comprehensively summarized the status and opinions of using ChatGPT across different nursing fields.

Virtual reality (VR) is increasingly being used in higher education for clinical skills training and role-playing among health care students. Using 360° videos in VR headsets, followed by peer debrief and group discussions, may strengthen students’ social and emotional learning.

With the rapid advancement of artificial intelligence (AI) in various fields, evaluating its application in specialized medical contexts becomes crucial. ChatGPT, a large language model developed by OpenAI, has shown potential in diverse applications, including medicine.

Ophthalmology residents take the Ophthalmic Knowledge Assessment Program (OKAP) exam annually, which provides percentile rank for multiple categories and the total score. Additionally, ophthalmology residency training programs have multiple subspecialty rotations with defined minimum procedure requirements. However, residents’ surgical volumes vary, with some residents exceeding their peers in specific subspecialty rotations.

Recent studies, including those by the National Board of Medical Examiners (NBME), have highlighted the remarkable capabilities of recent large language models (LLMs) such as ChatGPT in passing the United States Medical Licensing Examination (USMLE). However, there is a gap in detailed analysis of LLM performance in specific medical content areas, thus limiting an assessment of their potential utility in medical education.