The need for effective and accurate text summary models increases as the volume of digital textual information expands dramatically in both the general and medical sectors. Summarizing a text involves condensing a lengthy piece of writing into a concise overview, preserving the meaning and value of the material. It has been a focal point of natural language processing (NLP) research for quite some time.
Positive results were communicated through the introduction of neural networks and deep learning techniques, in particular, sequence-to-sequence models using encoder-decoder architectures for abstract generation. Compared to rule-based and statistical methods, the summaries generated by these approaches were more natural and contextually appropriate. The effort is made more difficult by the need to preserve the contextual and relational characteristics of such results and the desire for precision in therapeutic settings.
Researchers used and improved ChatGPT to summarize radiology reports. To take full advantage of ChatGPT’s in-context learning capability and continuously improve it through interaction, a new iterative optimization method is developed and implemented using rapid engineering. To be more precise, we use similarity search algorithms to create a dynamic message that incorporates pre-existing reports that are semantically and clinically comparable. ChatGPT is trained on these parallel reports to understand text descriptions and summaries of similar image manifestations.
Main contributions
- The search for similarities allows learning in the context of a language model (LLM) with sparse data. A dynamic indicator is developed that includes all the most relevant data for LLM by identifying the most comparable cases in the corpus.
- We created a system of dynamic indications for an iterative optimization technique. The iterative indicator first evaluates the responses generated by LLM and then provides further instructions to do so in subsequent iterations.
- A novel approach to LLM tuning that takes advantage of domain-specific information. The suggested methodology can be used when domain specific models need to be developed from an existing LLM quickly and effectively.
methods
Indication of variables
Dynamic samples employ semantic search to acquire instances of a report body comparable to the input radiology report; the final query comprises the same predefined query along with the “Findings” part of the test report, and the task description describes the role.
Optimization via iteration
Cool things can be done using the iterative optimization component. The goal of this approach is to allow ChatGPT to iteratively refine its response by using an iterative flag. Important for high risk applications such as radiology report summaries, this also requires a response review procedure to verify the quality of the responses.
The feasibility of using large language models (LLM) to summarize radiological reports is investigated by improving input prompts based on a small number of training samples and an iterative approach. The corpus is mined for appropriate instances to learn LLM in context, which are then used to provide interactive clues. To further improve the output, an iterative optimization technique is used. The procedure consists of 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 to be superior. In modern artificial general intelligence, this work also serves as the foundation for building more domain-specific language models.
While working on the ImpressionGPT iterative framework, we realized that evaluating the quality of model output responses is an essential but difficult task. The researchers hypothesize that large variations between domain-specific and domain-general text used to train LLMs contribute to the observed discrepancies in scores. Therefore, the examination of the details of the results obtained is enhanced by the use of detailed evaluation measures.
To better include domain-specific data from public and local data sources, we will continue to optimize the rapid design in the future while addressing data privacy and security concerns. Especially when it comes to many organizations. We also consider using the Knowledge Graph to tailor the request design to current domain knowledge. Ultimately, we plan to incorporate human specialists, such as radiologists, into the iterative process of optimizing indications and providing objective feedback on the results provided by the system. By combining the judgment and perspective of human specialists in LLM development, we are able to obtain more accurate results.
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Dhanshree Shenwai is a Computer Engineer and has good experience in FinTech companies covering Finance, Cards & Payments and Banking domain with strong interest in AI applications. She is enthusiastic about exploring new technologies and advancements in today’s changing world, making everyone’s life easier.
