Large Language Models and Medical Education: Preparing for a Rapid Transformation in How Trainees Will Learn to Be Doctors

Abstract

Artificial intelligence has the potential to revolutionize health care but has yet to be widely implemented. In part, this may be because, to date, we have focused on easily predicted rather than easily actionable problems. Large language models (LLMs) represent a paradigm shift in our approach to artificial intelligence because they are easily accessible and already being tested by frontline clinicians, who are rapidly identifying possible use cases. LLMs in health care have the potential to reduce clerical work, bridge gaps in patient education, and more. As we enter this era of healthcare delivery, LLMs will present both opportunities and challenges in medical education. Future models should be developed to support trainees to develop skills in clinical reasoning, encourage evidence-based medicine, and offer case-based training opportunities. LLMs may also change what we continue teaching trainees with regard to clinical documentation. Finally, trainees can help us train and develop the LLMs of the future as we consider the best ways to incorporate LLMs into medical education. Ready or not, LLMs will soon be integrated into various aspects of clinical practice, and we must work closely with students and educators to make sure these models are also built with trainees in mind to responsibly chaperone medical education into the next era.

Generative AI and Patient Care: A Systematic Review Examining Applications, Limitations, and Future Directions for ChatGPT in Healthcare

“…There are clearly emerging roles for AI tools in enhancing clinical team efficiency, including automating documentation and patient data processing [27,28]. Targeted applications in defined use cases appear more prudent at ChatGPT's current stage of development compared to open-ended clinical deployment.…”

Assessing ChatGPT’s Responses to Otolaryngology Patient Questions

“…The integration of artificial intelligence, particularly large language models like ChatGPT, into clinical practice has garnered significant attention in the healthcare community. 28 -30 While much of the existing literature highlights the potential positive impact of AI in healthcare, it is equally important to address the safety concerns associated with its use. Our evaluation of ChatGPT’s responses to otolaryngology-related patient questions sheds light on these concerns and underscores the need for a cautious approach to its general use and implementation in clinical practice.…”

Exploration and Practice of the Application of Large Models in Undergraduate Course Teaching of Economics and Management——"Introduction to Logistics Management" as an Example

“…The integration of a global perspective: Large models, due to their ability to process large-scale data, enable the teaching of logistics management to incorporate more global perspectives [10]. Students can understand the dynamics and challenges of global supply chains by simulating logistics cases from different countries and regions, which is particularly important for today's globalized and closely connected economic system.…”

“…Technical skills and training needs [10]: Teachers and educational practitioners may lack the technical skills and training required to use large-scale language models [9]. They need to learn how to effectively use these models and integrate them into teaching and curriculum design.…”