JMIR Medical Education

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.

Competence-based medical education requires robust data to link competence with clinical experiences. The SARS-CoV-2 pandemic abruptly altered the standard trajectory of clinical exposure in medical training programs. Residency program directors were tasked with identifying and addressing the resultant gaps in each trainee’s experiences using existing tools.

Abstract: Uncertainty is an inherent feature in the practice of medicine. Whether it is in understanding the patient’s problem, performing the physical examination, interpreting diagnostic tests or proposing a management plan, physicians are asked to make decisions on a daily basis without complete certainty. The sources of this uncertainty are widespread, and range from lack of knowledge about the patient, personal physician limitations, and limited predictive power of objective diagnostic tools. This uncertainty poses significant problems in providing competent patient care. Research efforts and teaching are attempts to reduce uncertainty that have now become inherent to medicine. Despite this, uncertainty is rampant. Artificial intelligence tools, which are being rapidly developed and integrated into practice, may change the way we navigate uncertainty. In their strongest forms, artificial intelligence tools may have the ability to improve data collection on diseases, patient beliefs, values and preferences, and allow more time for physician-patient communication. These tools hold potential to improve reducible forms of uncertainty in medicine, such as those due to lack of clinical information and provider skill and bias, using methods not previously considered. Despite this, there has been considerable resistance to the implementation of AI tools in medical practice. In this viewpoint article, we discuss the impact of artificial intelligence on medical uncertainty and discuss practical approaches to teaching the use of artificial intelligence tools in medical schools and residency training programs, including AI ethics, practical skills and technological aptitude.

Undergraduate medical students often lack hands-on research experience and fundamental scientific research skills, limiting their exposure to the practical aspects of scientific investigation. The Cerrahpasa Neuroscience Society introduced a program to address this deficiency and facilitate student-led research.

Digital transformation has disrupted many industries but is yet to revolutionise healthcare. Educational programs must be aligned to the reality that beyond developing individuals in their own professions, professionals wishing to make an impact in digital health will need a multidisciplinary understanding of how business models, organisational processes, stakeholder relationships, and workforce dynamics across the healthcare ecosystem may be disrupted by digital health technology.