---
title: "Evaluation of generative AI techniques for clinical report summarization"
url: https://daily.dev/posts/evaluation-of-generative-ai-techniques-for-clinical-report-summarization-29t3deguy
source_url: https://aws.amazon.com/blogs/machine-learning/evaluation-of-generative-ai-techniques-for-clinical-report-summarization/
type: article
source: "AWS"
published: 2024-05-13T19:23:57.058Z
updated: 2024-05-13T19:23:56.011Z
tags: ["genai", "prompt-engineering", "amazon-bedrock"]
reading_time: 11
upvotes: 0
comments: 0
language: en
---

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# Evaluation of generative AI techniques for clinical report summarization

**[AWS](https://daily.dev/sources/aws)** · 11 min read · 0 upvotes · 0 comments

## Summary

This post evaluates generative AI techniques, such as zero-shot and few-shot prompting, and the Retrieval Augmented Generation (RAG) prompt engineering technique for clinical report summarization. The evaluation results show that the RAG approach performs the best among the techniques. The post also provides an overview of prompt engineering techniques, such as prompt structure and prompt templates, and discusses the dataset and experimental setup. Additionally, the post highlights the performance analysis of the techniques using the ROUGE score.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://aws.amazon.com/blogs/machine-learning/evaluation-of-generative-ai-techniques-for-clinical-report-summarization/>

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---

Tags: [#genai](https://daily.dev/tags/genai), [#prompt-engineering](https://daily.dev/tags/prompt-engineering), [#amazon-bedrock](https://daily.dev/tags/amazon-bedrock)

[View this post on daily.dev](https://daily.dev/posts/evaluation-of-generative-ai-techniques-for-clinical-report-summarization-29t3deguy)