Meet ImpressionGPT: A ChatGPT-Based Iterative Optimization Framework for Radiology Report Summaries

The need for effective and accurate text summarization models increases as the volume of digital textual information expands incredibly in both the general and medical sectors. Summarizing text involves condensing a lengthy piece of writing into a concise overview while retaining the material’s meaning and value. It’s been a focal point of Natural Language Processing (NLP) research for quite some time.

Positive outcomes were communicated by introducing neural networks and deep learning techniques, particularly sequence-to-sequence models using encoder-decoder architectures for summary generation. Compared to rule-based and statistical methods, the summaries generated by these approaches were more natural and contextually appropriate. The endeavor is made more difficult because of the need to preserve such results’ contextual and relational features and the desire for precision in therapeutic settings.

ChatGPT was used and improved by researchers to summarize radiological reports. To make the most of ChatGPT’s in-context learning capability and to continually improve it through interaction, a novel iterative optimization method is developed and implemented using rapid engineering. To be more precise, we employ similarity search algorithms to build a dynamic prompt that incorporates preexisting reports that are semantically and clinically comparable. ChatGPT is trained with these parallel reports to understand similar imaging manifestations’ text descriptions and summaries.

Main Contributions

• Similarity search enables in-context learning of a Language Model (LLM) with sparse data. A dynamic prompt that includes all the most relevant data for LLM is developed by identifying the most comparable cases in the corpus.

• We create a dynamic prompting system for an iterative optimization technique. The iterative prompt first evaluates the LLM-generated replies and then gives more directions for doing so in subsequent iterations.

• A novel approach to LLM tweaking that capitalizes on domain-specific information. The suggested methodology may be used when domain-specific models need to be developed from an existing LLM quickly and effectively.

Methods

Variable Prompt

Dynamic samples employ semantic search to acquire examples from a report corpus comparable to the input radiology report; the final query comprises the same pre-defined inquiry paired with the “Findings” part of the test report, and the task description describes the role.

Optimization via Iteration

Cool stuff can be done using the iterative optimization component. The goal of this approach is to allow ChatGPT to iteratively refine its answer through the use of an iterative prompt. Important for high-stakes applications like radiology report summaries, this also requires a response review procedure to check the quality of replies.

The feasibility of using Large Language Models (LLMs) for summarizing radiological reports is investigated by enhancing the input prompts based on a small number of training samples and an iterative method. The corpus is mined for appropriate instances to learn LLMs in context, which are then used to provide interactive cues. To further enhance the output, an iterative optimization technique is used. The procedure entails teaching the LLM what constitutes a good and negative response based on automated evaluation feedback. Compared to other approaches that use massive amounts of medical text data for pre-training, our strategy has proven superior. In modern artificial general intelligence, this work also serves as a foundation to build further domain-specific language models.

While working on the iterative framework of ImpressionGPT, we realized that assessing the quality of the model’s output responses is an essential but difficult task. Researchers hypothesize that the vast variances between domain-specific and general-domain text used to train LLMs contribute to the observed discrepancies in the scores. Therefore, examining the specifics of the obtained outcomes is enhanced by employing fine-grained assessment measures.

To better include the domain-specific data from both public and local data sources, we will continue optimizing the quick design in the future while addressing data privacy and safety issues. Especially when dealing with many organizations. We also consider using Knowledge Graph to adapt the prompt design to current domain knowledge. Finally, we plan to incorporate human specialists, such as radiologists, into the iterative process of optimizing the prompts and providing objective feedback on the outcomes provided by the system. By combining the judgment and perspective of human specialists in developing LLMs, we can get more precise results.

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