Generalist embedding models are better at short-context clinical semantic search than specialized embedding models
This addresses the reliability of AI tools in the critical medical domain, but it is incremental as it benchmarks existing models without proposing new methods.
The study tackled the problem of robustness and reliability of embedding models in clinical semantic search by benchmarking generalist and specialized models on a dataset of ICD-10-CM code descriptions and their rephrasings, finding that generalist models performed better, indicating specialized models are more sensitive to input variations.
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.