Enhancing Summarization Performance through Transformer-Based Prompt Engineering in Automated Medical Reporting (2311.13274v2)

Abstract: Customized medical prompts enable LLMs (LLM) to effectively address medical dialogue summarization. The process of medical reporting is often time-consuming for healthcare professionals. Implementing medical dialogue summarization techniques presents a viable solution to alleviate this time constraint by generating automated medical reports. The effectiveness of LLMs in this process is significantly influenced by the formulation of the prompt, which plays a crucial role in determining the quality and relevance of the generated reports. In this research, we used a combination of two distinct prompting strategies, known as shot prompting and pattern prompting to enhance the performance of automated medical reporting. The evaluation of the automated medical reports is carried out using the ROUGE score and a human evaluation with the help of an expert panel. The two-shot prompting approach in combination with scope and domain context outperforms other methods and achieves the highest score when compared to the human reference set by a general practitioner. However, the automated reports are approximately twice as long as the human references, due to the addition of both redundant and relevant statements that are added to the report.

• The paper’s main contribution is demonstrating that combining two-shot prompting with structured pattern prompting significantly improves report quality through higher ROUGE scores and human validation.
• The study employed transformer-based LLMs utilizing both shot and pattern prompt engineering to reduce redundancy and enhance factual accuracy in automated medical reporting.
• The findings underscore the need for further prompt optimization to address remaining factual and stylistic errors, advancing the integration of AI in healthcare.

Introduction

AI is increasingly integral to medical decision-making and healthcare service provision. The application of LLMs in the medical domain is expanding rapidly and includes tasks such as medical reporting, which is a vital component of patient care. Automating the reporting process helps to alleviate the administrative load on healthcare professionals, making the role of prompt engineering crucial for optimizing LLM performance in generating accurate and relevant medical reports.

Prompt Engineering Methodologies

The effectiveness of LLMs, such as Generative Pre-trained Transformers (GPT), in creating medical reports can be influenced by prompt engineering - an essential aspect of AI-led healthcare applications. This research focuses on two distinct prompting strategies: shot prompting and pattern prompting. Shot prompting uses in-context learning, giving the model examples as guidance. One-shot and two-shot strategies offer the model increasing amounts of context, which has shown to improve the model's output. Pattern prompting leverages various structured formats or patterns that help better define the context for LLMs, extending their applicability across a range of scenarios.

Summarization Performance and Evaluation

The paper meticulously experiments with a combination of shot prompting and context patterns. It was observed that utilizing two-shot prompting as a base and layering it with domain-specific contexts resulted in the most effective performance improvements. Reports generated with such reinforced prompts were subjected to scoring using the ROUGE metric and also underwent a critical human evaluation comparing their content with a set of human-prepared reference reports. The human evaluation assessed the occurrence of several types of logical and stylistic errors, including redundancy, factual accuracy, and omission of critical details.

Discussion and Findings

Presenting the results of automated medical reporting performance, the paper indicates that a combination of shot prompting and context patterns provides the highest quality output, as evidenced by higher ROUGE scores. Moreover, the human evaluation unearthed that while these reports significantly reduced redundant information, factual or stylistic errors remained present, reflecting the ongoing challenges in automating medical report generation. Consequently, these findings provide valuable insights and underscore the necessity for further optimization in prompt engineering to enhance the reliability of automated medical reports in clinical settings.

In conclusion, the paper advances the methodology of prompt engineering within the healthcare domain, demonstrating that while existing strategies contribute positively to LLMs' performance in medical report generation, refinements are crucial for achieving professional satisfaction and usability. As AI continues to infiltrate the healthcare domain, the importance of meticulous prompt optimization is only set to increase.