Generative Artificial Intelligence and Large Language Models in Primary Care Medical Education

Area of Science:

• Artificial Intelligence in Education
• Medical Training Technologies
• Information Processing Innovations

Background:

• Generative AI and large language models (LLMs) represent a significant advancement in information processing, building upon foundational technologies like the transistor.
• These AI technologies, powered by transformer architectures, are expected to profoundly impact various societal sectors, including education.
• Medical education presents unique challenges due to the high-stakes nature of information transfer, where errors can lead to patient harm.

Purpose of the Study:

• To discuss the primary limitations of using generative AI in medical education.
• To propose strategies for overcoming these identified challenges.
• To explore the potential applications of generative AI within the medical education landscape.

Main Methods:

• Discussion of principal limitations: hallucination, bias, cost, and security.
• Suggestion of approaches to mitigate these limitations.
• Identification of potential applications in medical education.

Main Results:

• Key limitations identified include AI hallucination, inherent biases, financial costs, and data security concerns.
• Potential strategies for addressing these limitations are proposed.
• Numerous applications are identified, ranging from personalized learning to critical assessment of scientific literature.

Conclusions:

• Generative AI holds significant promise for revolutionizing medical education through personalized instruction, enhanced simulation, and improved feedback mechanisms.
• Careful consideration and mitigation of AI's limitations are crucial for its safe and effective integration into medical training.
• The responsible adoption of generative AI can augment qualitative research and critical appraisal of medical literature, ultimately benefiting patient care.