From Posters to Prompting: Reimagining CME in the Era of Artificial Intelligence

Medical education is evolving rapidly alongside the emergence of artificial intelligence (AI) and large language models (LLMs). Continuing medical education (CME) has traditionally relied on educational modalities sometimes conceptualized as the “three P’s” of Posters, Podium presentations, and Publications, later expanded into the “seven P’s” with the addition of Podcasts, Play, Peer-to-peer learning, and Patient interactions. This commentary proposes Prompting as an eighth “P” of CME, reflecting the growing importance of effectively interacting with AI systems in clinical and academic practice. Prompting refers to the process of crafting and refining instructions that guide AI-generated outputs. The quality, specificity, and contextual framing of prompts substantially influence the accuracy and usefulness of responses generated by LLMs. Emerging evidence suggests that prompting is a learnable skill that can improve educational and clinical utility without requiring advanced technical expertise. This commentary reviews key prompting strategies, including zero-shot, few-shot, role-based, chain-of-thought, and iterative prompting, and discusses their relevance to healthcare providers. Applications across clinical care, medical education, patient communication, administration, and research are explored. Potential benefits include generating differential diagnoses, summarizing medical literature, creating educational materials, supporting clinical simulations, and improving patient-facing communication. The commentary also addresses important risks associated with LLM use, including hallucinations, automation bias, and overreliance on AI-generated content. Ultimately, prompting is framed not only as a mechanism for learning through AI systems, but also as a modern medical literacy competency that healthcare providers must actively develop as AI becomes increasingly integrated into healthcare and medical education.