The increasing use of tools and solutions based on Large Language Models
(LLMs) for various tasks in the medical domain has become a prominent trend.
Their use in this highly critical and sensitive domain has thus raised
important questions about their robustness, especially in response to
variations in input, and the reliability of the generated outputs. This study
addresses these questions by constructing a textual dataset based on the
ICD-10-CM code descriptions, widely used in US hospitals and containing many
clinical terms, and their easily reproducible rephrasing. We then benchmarked
existing embedding models, either generalist or specialized in the clinical
domain, in a semantic search task where the goal was to correctly match the
rephrased text to the original description. Our results showed that generalist
models performed better than clinical models, suggesting that existing clinical
specialized models are more sensitive to small changes in input that confuse
them. The highlighted problem of specialized models may be due to the fact that
they have not been trained on sufficient data, and in particular on datasets
that are not diverse enough to have a reliable global language understanding,
which is still necessary for accurate handling of medical documents.
The increasing use of tools and solutions based on Large Language Models (LLMs) in the medical domain is a significant trend. However, the robustness and reliability of these models has become a concern, especially when faced with variations in input. This study aims to address these concerns and shed light on the performance of existing embedding models in the clinical domain.
Benchmarking Embedding Models
In this study, a textual dataset was created based on the ICD-10-CM code descriptions, which are widely used in US hospitals and contain numerous clinical terms. The dataset consisted of original descriptions and their reproducible rephrasing. The goal of the study was to evaluate the performance of existing embedding models in a semantic search task, where the objective was to match the rephrased text to the original description accurately.
Generalist Models vs. Clinical Models
The study compared generalist models, which are not specialized in any specific domain, with clinical models that are specifically trained on medical data. Surprisingly, the generalist models outperformed the clinical models in this task. This finding suggests that existing clinical models may be more sensitive to small changes in input that can cause confusion. It is essential to explore why specialized models exhibit this behavior and whether it is due to a lack of sufficient training data.
Diversity in Training Data
This study raises an important point about the importance of diverse training data for reliable global language understanding in handling medical documents. Specialized models might not have been trained on datasets that adequately represent the wide range of language variations encountered in medical settings.
Multi-disciplinary Nature
The content discussed in this article touches upon multiple disciplines, including natural language processing, machine learning, and medicine. By evaluating the performance of embedding models in a clinical context, it provides insights into the challenges faced when applying language models in highly specialized domains like medicine.
In conclusion, this study highlights the need for further research and development of embedding models, specifically in the medical domain. By addressing the concerns related to robustness and reliability, we can unlock the full potential of Large Language Models for tasks in the medical field.