Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial

The advent of large language models (LLMs), particularly ChatGPT, has revolutionized artificial intelligence (AI) accessibility. This paradigm shift significantly impacts healthcare, with LLMs offering potential benefits in various medical tasks, including decision support, administrative assistance, patient engagement, research, education, and public health initiatives. However, leveraging LLMs effectively requires a new skill: prompt engineering. Prompt engineering involves designing and refining prompts to guide LLM output, optimizing their use for specific applications. This paper examines the current state of prompt engineering research and provides practical guidance for medical professionals to improve their interactions with LLMs in healthcare settings.

Existing research on prompt engineering within the medical field is limited. While studies have explored prompt engineering techniques for general problem-solving with LLMs and specific applications in medical natural language processing (NLP), a comprehensive, practical guide tailored for medical professionals is lacking. This paper aims to fill this gap by summarizing existing research and providing practical recommendations.

This tutorial-style paper synthesizes current knowledge on prompt engineering and provides practical recommendations for medical professionals. It discusses the underlying principles of LLMs, highlighting the importance of understanding their capabilities and limitations. The paper presents specific strategies for crafting effective prompts, illustrated with practical examples using ChatGPT. These strategies include being specific, providing context, experimenting with prompt styles, identifying goals, using role-playing, utilizing threads, iterating and refining prompts, asking open-ended questions, requesting examples, setting realistic expectations, and employing one-shot/few-shot prompting methods. Additionally, the paper addresses major limitations of ChatGPT, including its reliance on outdated data, lack of medical expertise, potential for hallucinations, and privacy concerns, emphasizing the importance of verifying LLM responses with qualified professionals. The paper also lists popular plugins relevant to healthcare professionals, such as those that enhance scientific literature searching and summarization. Finally, it suggests the inclusion of prompt engineering training in medical curricula.

The paper identifies prompt engineering as a critical emerging skill for medical professionals. It highlights the importance of understanding the underlying principles of LLMs and their limitations. Specific recommendations include: (1) Being as specific as possible in prompts, (2) Providing context and setting, (3) Experimenting with different prompt styles (e.g., direct questions, requests for lists or summaries), (4) Identifying the overall goal of the prompt, (5) Using role-playing to elicit specific types of responses, (6) Utilizing threads for iterative clarification, (7) Iteratively refining prompts based on feedback, (8) Asking open-ended questions for comprehensive answers, (9) Requesting specific examples, (10) Setting realistic expectations, and (11) Utilizing one-shot/few-shot prompting. The paper also emphasizes the critical need to always verify LLM-generated information with qualified medical professionals. Furthermore, it underscores the potential of LLMs as valuable tools for enhancing healthcare professionals' knowledge and capabilities rather than replacing human judgment. Popular plugins for healthcare professionals, such as ScholarAI and AskYourPDF, are also recommended. The study advocates for incorporating prompt engineering education into medical curricula.

The widespread adoption of LLMs in healthcare necessitates the development of effective strategies for their use. This paper's emphasis on prompt engineering provides a practical framework for improving human-LLM interaction in medical contexts. By understanding LLM capabilities and limitations and employing the recommended strategies, medical professionals can better leverage these tools for various tasks. This includes improving the accuracy and efficiency of diagnoses, streamlining administrative processes, enhancing patient communication, advancing research, and improving medical education. The call for integrating prompt engineering into medical education underscores the significance of this skill for the future of healthcare.

Prompt engineering is an essential emerging skill for medical professionals to effectively utilize LLMs in healthcare. The recommendations provided in this paper, if integrated into medical education and practice, can significantly improve the use of LLMs, enhancing healthcare efficiency, accuracy, and effectiveness. Future research should focus on developing more advanced prompt engineering techniques and evaluating the impact of prompt engineering training on healthcare outcomes.

This paper primarily focuses on practical recommendations for prompt engineering with a specific LLM (ChatGPT). The strategies presented may require adaptation depending on the specific LLM and healthcare application. Further research is needed to comprehensively evaluate the effectiveness of different prompt engineering techniques across various healthcare settings and LLMs. The paper also doesn't extensively address the ethical implications of using LLMs in healthcare, although it mentions the risks associated with privacy and accuracy.