Evaluation of generative AI techniques for clinical report summarization

This article examines various generative AI techniques for summarizing clinical reports, particularly radiology reports. It compares the performance of zero-shot prompting, few-shot prompting, and the Retrieval Augmented Generation (RAG) pattern for this task.

Specifically, the article covers:

• Overview of the problem and solution approach
• Description of the pre-trained language models (LLMs), dataset, and evaluation metric (ROUGE) used
• Techniques evaluated: zero-shot prompting, few-shot prompting, and RAG pattern
• Implementation details for each technique, including prompt structure and examples
• Evaluation results comparing the performance of different techniques on two datasets (dev1 and dev2)
• Conclusion highlighting the strengths and limitations of each approach