Harnessing Large Language Models for Medical Applications: A Scoping Review of Prompt Engineering Techniques and Recommendations

Core Concepts

Prompt engineering is crucial for leveraging the potential of large language models in the medical domain, but its efficacy remains to be systematically explored. This scoping review analyzes the definitions, methodologies, techniques, and outcomes of prompt engineering across various medical NLP tasks.

Abstract

This scoping review examines the current landscape of prompt engineering research in the medical field. The key insights are:

Prompt Design (PD) is the most prevalent prompt engineering paradigm, with 78 articles spanning various medical specialties. Prompt Learning (PL) and Prompt Tuning (PT) are also explored, but to a lesser extent.

ChatGPT is the most widely used large language model, with 7 studies using it to process sensitive clinical data. Chain-of-Thought emerges as the most common prompt engineering technique.

While PL and PT articles typically provide a baseline for evaluating prompt-based approaches, 64% of PD studies lack non-prompt-related baselines, limiting the understanding of the actual impact of prompt engineering.

The review identifies terminology inconsistencies, with 12 studies using the prompt engineering terms interchangeably. It also highlights the dominance of English, with 84.2% of the articles studying English, and the lack of explicit language reporting, especially in computer science and clinical venues.

The authors provide detailed reporting guidelines to improve transparency and reproducibility in future prompt engineering studies in the medical domain.

Stats

"Prompt engineering is crucial for harnessing the potential of large language models (LLMs), especially in the medical domain where specialized terminology and phrasing is used."
"ChatGPT is the most used LLM, with seven papers using it for processing sensitive clinical data."
"64% of PD studies lack non-prompt-related baselines."

Quotes

"Prompt engineering encompasses a plethora of techniques, often separated into distinct categories such as output customization and prompt improvement."
"Chain-of-Thought emerges as the most common prompt engineering technique."
"Notably, 22 articles present results using only one prompt choice, without clarifying whether this choice was made thanks to additional validation datasets."

Key Insights Distilled From

Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices

by Jami... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01249.pdf

Deeper Inquiries

How can prompt engineering techniques be further improved to better capture the nuances of medical language and domain-specific knowledge?

Prompt engineering techniques can be enhanced to better capture the intricacies of medical language and domain-specific knowledge by incorporating the following strategies:

Domain-specific Prompt Libraries: Developing specialized prompt libraries tailored to medical terminology and context can assist in formulating prompts that align closely with the nuances of the medical field. These libraries can include pre-defined prompts for common medical tasks, ensuring accuracy and relevance in prompt design.

Collaboration with Medical Experts: Engaging healthcare professionals and domain experts in the prompt engineering process can provide valuable insights into the specific language, terminology, and requirements of medical tasks. Collaborating with clinicians can help refine prompts to better reflect the complexities of medical scenarios.

Fine-tuning for Medical Tasks: Implementing fine-tuning techniques that focus on medical tasks can optimize prompts for specific healthcare applications. By training LLMs on medical datasets and fine-tuning prompts for medical language understanding, the models can better capture the nuances of medical information.

Contextual Prompting: Incorporating contextual information into prompts can enhance the understanding of medical language. By providing relevant context along with prompts, LLMs can better interpret and generate accurate responses in medical scenarios.

Evaluation and Validation: Implementing robust evaluation metrics and validation processes specific to medical tasks can ensure the effectiveness of prompt engineering techniques. Continuous validation with medical professionals and benchmarking against established standards can help refine prompt designs for optimal performance in healthcare applications.

Ethical Considerations: Considering the ethical implications of prompt engineering in healthcare is crucial. Ensuring that prompts do not inadvertently introduce bias, misinformation, or privacy concerns is essential for maintaining the integrity and trustworthiness of medical language models.

By integrating these strategies into prompt engineering practices, researchers can enhance the effectiveness and accuracy of LLMs in capturing the nuances of medical language and domain-specific knowledge.

How can the potential ethical and privacy concerns associated with using large language models like ChatGPT to process sensitive clinical data be addressed?

The utilization of large language models like ChatGPT to process sensitive clinical data raises significant ethical and privacy concerns that must be addressed. Here are some strategies to mitigate these challenges:

Data Security Measures: Implement robust data security measures to safeguard sensitive clinical information processed by LLMs. This includes encryption, access controls, and secure data storage practices to prevent unauthorized access or data breaches.

Anonymization and De-identification: Prior to processing clinical data, ensure that all personally identifiable information is anonymized or de-identified to protect patient privacy. Adopting best practices for data anonymization can minimize the risk of re-identification and unauthorized disclosure.

Ethical Guidelines and Compliance: Adhere to established ethical guidelines and regulatory frameworks governing the use of medical data, such as HIPAA in the United States or GDPR in the European Union. Compliance with these regulations ensures that patient privacy and confidentiality are maintained during data processing.

Transparency and Informed Consent: Maintain transparency in the use of LLMs for processing clinical data and obtain informed consent from patients or data subjects. Clearly communicate how their data will be used, the purpose of processing, and any potential risks involved.

Bias Detection and Mitigation: Regularly assess LLMs for biases that may impact the processing of clinical data, particularly in sensitive healthcare contexts. Implement bias detection mechanisms and strategies to mitigate any biases that could lead to discriminatory outcomes.

Accountability and Oversight: Establish clear accountability structures and oversight mechanisms to monitor the use of LLMs in processing clinical data. Designate responsible individuals or committees to ensure compliance with ethical standards and privacy regulations.

Continuous Monitoring and Auditing: Conduct regular audits and monitoring of LLM activities to identify any privacy or ethical issues that may arise. Implement mechanisms for ongoing evaluation and improvement to address emerging concerns proactively.

By incorporating these measures into the deployment of large language models like ChatGPT for processing sensitive clinical data, healthcare organizations can uphold ethical standards, protect patient privacy, and mitigate potential risks associated with the use of LLMs in healthcare settings.

How can prompt engineering research be expanded to support a wider range of languages and medical contexts globally?

Expanding prompt engineering research to encompass a broader range of languages and medical contexts globally can be achieved through the following strategies:

Multilingual Prompt Development: Develop multilingual prompt libraries that cater to diverse language requirements in medical contexts. By creating prompts in multiple languages, researchers can support a more inclusive and global approach to prompt engineering.

Collaboration with Linguistic Experts: Collaborate with linguistic experts and translators proficient in various languages to ensure the accuracy and relevance of prompts across different linguistic backgrounds. Incorporating linguistic diversity in prompt design can enhance the applicability of prompt engineering in global healthcare settings.

Localization of Prompts: Localize prompts to specific regional dialects and linguistic nuances to better align with the cultural and linguistic variations present in different medical contexts worldwide. Adapting prompts to local language preferences can improve the effectiveness of prompt engineering in diverse healthcare environments.

Cross-Cultural Validation: Validate prompts across different cultural and linguistic settings to assess their effectiveness and adaptability in varied medical contexts. Conducting cross-cultural studies can help identify language-specific challenges and optimize prompts for global applicability.

Open Access Resources: Share multilingual prompt datasets and resources openly to facilitate research collaboration and knowledge exchange in prompt engineering. Open access to multilingual prompt libraries can encourage researchers worldwide to contribute to the advancement of prompt design in diverse languages.

Training and Capacity Building: Provide training programs and capacity-building initiatives focused on prompt engineering in different languages and medical contexts. Empowering researchers and practitioners with the necessary skills and resources can foster innovation and collaboration in global prompt engineering research.

Community Engagement: Engage with international research communities, healthcare organizations, and language experts to promote awareness and participation in multilingual prompt engineering research. Building a global network of stakeholders can drive collective efforts towards expanding prompt engineering to support a wider range of languages and medical contexts globally.

By embracing these strategies, prompt engineering research can extend its reach to diverse linguistic landscapes and healthcare settings worldwide, fostering innovation and inclusivity in the development of language models for global healthcare applications.