JMIR Medical Education

The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education. As we navigate the shift from an information-driven educational paradigm to an artificial intelligence (AI)–driven educational paradigm, we argue that it is paramount to understand both the potential and the pitfalls of LLMs in medical education. This paper thus offers our perspective on the opportunities and challenges of using LLMs in this context. We believe that the insights gleaned from this analysis will serve as a foundation for future recommendations and best practices in the field, fostering the responsible and effective use of AI technologies in medical education.

Graduate students in medical fields must learn about epidemiology and data analysis to conduct their research. R is a software environment used to develop and run packages for statistical analysis; it can be challenging for students to learn because of compatibility with their computers and problems with package installations. Jupyter Notebook was used to run R, which enhanced the graduate students’ ability to learn epidemiological data analysis by providing an interactive and collaborative environment that allows for more efficient and effective learning.

Single-choice items (eg, best-answer items, alternate-choice items, single true-false items) are 1 type of multiple-choice items and have been used in examinations for over 100 years. At the end of every examination, the examinees’ responses have to be analyzed and scored to derive information about examinees’ true knowledge.

Near-peer teaching (NPT) is becoming an increasingly popular pedagogical tool in health professions education. Despite the shift in formal medical education from face-to-face teaching toward encompassing web-based learning activities, NPT has not experienced a similar transition. Apart from the few reports on NPT programs hastily converted to web-based learning in light of the COVID-19 pandemic, no studies to date have explored web-based learning in the specific context of NPT.

Electronic health records (EHRs) play a substantial role in modern health care, especially during prerounding, when residents gather patient information to inform daily care decisions of the care team. The effective use of the EHR system is crucial for efficient and frustration-free prerounding. Ideally, the system should be designed to support efficient user interactions by presenting data effectively and providing easy navigation between different pages. Additionally, training on the system should aim to make user interactions more efficient by familiarizing the users with best practices that minimize interaction time while using the full potential of the system’s capabilities. However, formal training on EHR systems often falls short of providing residents with all the necessary EHR-related skills, leading to the adoption of inefficient practices and the underuse of the system’s full range of capabilities.

Telemedicine use increased as a response to health care delivery changes necessitated by the COVID-19 pandemic. However, lack of standardized curricular content creates gaps and inconsistencies in effectively integrating telemedicine training at both the undergraduate medical education and graduate medical education levels.

A successful periodontitis treatment demands good manual skills. A correlation between biological sex and dental students’ manual dexterity is currently unknown.

Large language models, such as ChatGPT by OpenAI, have demonstrated potential in various applications, including medical education. Previous studies have assessed ChatGPT’s performance in university or professional settings. However, the model’s potential in the context of standardized admission tests remains unexplored.

The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context.

Large language models exhibiting human-level performance in specialized tasks are emerging; examples include Generative Pretrained Transformer 3.5, which underlies the processing of ChatGPT. Rigorous trials are required to understand the capabilities of emerging technology, so that innovation can be directed to benefit patients and practitioners.

The COVID-19 pandemic was accompanied by the spread of uncontrolled health information and fake news, which also quickly became an infodemic. Emergency communication is a challenge for public health institutions to engage the public during disease outbreaks. Health professionals need a high level of digital health literacy (DHL) to cope with difficulties; therefore, efforts should be made to address this issue starting from undergraduate medical students.

Prolonged grief disorder (PGD) is a newly recognized mental disorder characterized by pervasive intense grief that persists longer than cultural or social expectations and interferes with functioning. The COVID-19 epidemic has resulted in increased rates of PGD, and few clinicians feel confident in treating this condition. PGD therapy (PGDT) is a simple, short-term, and evidence-based treatment developed in tandem with the validation of the PGD diagnosis. To facilitate the dissemination of PGDT training, we developed a web-based therapist tutorial that includes didactic training on PGDT concepts and principles as well as web-based multimedia patient scenarios and examples of clinical implementation of PGDT.