Artificial intelligence-powered generative language models (GLMs), such as ChatGPT, Perplexity AI, and Google Bard, have the potential to provide personalized learning, unlimited practice opportunities, and interactive engagement 24/7, with immediate feedback. However, to fully utilize GLMs, properly formulated instructions are essential. Prompt engineering is a systematic approach to effectively communicating with GLMs to achieve the desired results. Well-crafted prompts yield good responses from the GLM, while poorly constructed prompts will lead to unsatisfactory responses. Besides the challenges of prompt engineering, significant concerns are associated with using GLMs in medical education, including ensuring accuracy, mitigating bias, maintaining privacy, and avoiding excessive reliance on technology. Future directions involve developing more sophisticated prompt engineering techniques, integrating GLMs with other technologies, creating personalized learning pathways, and researching the effectiveness of GLMs in medical education.

The rapid advancements in artificial intelligence, particularly generative AI and large language models, have unlocked new possibilities for revolutionizing healthcare delivery. However, harnessing the full potential of these technologies requires effective prompt engineering—designing and optimizing input prompts to guide AI systems toward generating clinically relevant and accurate outputs. Despite the importance of prompt engineering, medical education has yet to fully incorporate comprehensive training on this critical skill, leading to a knowledge gap among medical clinicians. This article addresses this educational gap by providing an overview of generative AI prompt engineering, its potential applications in primary care medicine, and best practices for its effective implementation. The role of well-crafted prompts in eliciting accurate, relevant, and valuable responses from AI models is discussed, emphasizing the need for prompts grounded in medical knowledge and aligned with evidence-based guidelines. The article explores various applications of prompt engineering in primary care, including enhancing patient–provider communication, streamlining clinical documentation, supporting medical education, and facilitating personalized care and shared decision-making. Incorporating domain-specific knowledge, engaging in iterative refinement and validation of prompts, and addressing ethical considerations and potential biases are highlighted. Embracing prompt engineering as a core competency in medical education will be crucial for successfully adopting and implementing AI technologies in primary care, ultimately leading to improved patient outcomes and enhanced healthcare delivery.

Background Generative artificial intelligence (AI) models, exemplified by systems such as ChatGPT, Bard, and Anthropic, are currently under intense investigation for their potential to address existing gaps in mental health support. One implementation of these large language models involves the development of mental health-focused conversational agents, which utilize pre-structured prompts to facilitate user interaction without requiring specialized knowledge in prompt engineering. However, uncertainties persist regarding the safety and efficacy of these agents in recognizing severe depression and suicidal tendencies. Given the well-established correlation between the severity of depression and the risk of suicide, improperly calibrated conversational agents may inadequately identify and respond to crises. Consequently, it is crucial to investigate whether publicly accessible repositories of mental health-focused conversational agents can consistently and safely address crisis scenarios before considering their adoption in clinical settings. This study assesses the safety of publicly available ChatGPT-3.5 conversational agents by evaluating their responses to a patient simulation indicating worsening depression and suicidality. Methodology This study evaluated ChatGPT-3.5 conversational agents on a publicly available repository specifically designed for mental health counseling. Each conversational agent was evaluated twice by a highly structured patient simulation. First, the simulation indicated escalating suicide risk based on the Patient Health Questionnaire (PHQ-9). For the second patient simulation, the escalating risk was presented in a more generalized manner not associated with an existing risk scale to assess the more generalized ability of the conversational agent to recognize suicidality. Each simulation recorded the exact point at which the conversational agent recommended human support. Then, the simulation continued until the conversational agent stopped entirely and shut down completely, insisting on human intervention. Results All 25 agents available on the public repository FlowGPT.com were evaluated. The point at which the conversational agents referred to a human occurred around the mid-point of the simulation, and definitive shutdown predominantly only happened at the highest risk levels. For the PHQ-9 simulation, the average initial referral and shutdown aligned with PHQ-9 scores of 12 (moderate depression) and 25 (severe depression). Few agents included crisis resources - only two referenced suicide hotlines. Despite the conversational agents insisting on human intervention, 22 out of 25 agents would eventually resume the dialogue if the simulation reverted to a lower risk level. Conclusions Current generative AI-based conversational agents are slow to escalate mental health risk scenarios, postponing referral to a human to potentially dangerous levels. More rigorous testing and oversight of conversational agents are needed before deployment in mental healthcare settings. Additionally, further investigation should explore if sustained engagement worsens outcomes and whether enhanced accessibility outweighs the risks of improper escalation. Advancing AI safety in mental health remains imperative as these technologies continue rapidly advancing.

VOICE Credit: This activity has been approved for 1.0 VOICE (Category A) credit. For CE credit, participants can access this activity on page 17A or on the SNM Web site (http://www.snm.org/ce_online) through December 31, 2012. You must answer 80% of the questions correctly to receive 1.0 CEH (Continuing Education Hour) credit.

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