Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for Domain Specialized Text Analysis

The analysis of medical texts is a key component of healthcare informatics, where the accurate comparison and interpretation of documents can significantly impact patient care and medical research. Traditionally, this analysis has leveraged lexical comparison metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) [1] and BLEU (Bilingual Evaluation Understudy) [2], which have become standard tools in the evaluation of text similarity within the domain of natural language processing (NLP). ROUGE and BLEU were initially designed to assess the quality of automatic summarization and machine translation respectively, by measuring the overlap of n-grams between the generated texts and reference texts. While these metrics have been instrumental in advancing NLP applications, their application in medical text analysis reveals inherent limitations. Specifically, ROUGE and BLEU focus predominantly on surface-level lexical similarities, often overlooking the deep semantic meanings and clinical implications embedded within medical documents. This gap in capturing the essence and context of medical language presents a significant challenge in leveraging these metrics for meaningful analysis in healthcare. Recognizing these limitations, this research proposes a novel methodology that employs GPT-4, a state-of-the-art large language model, for a more sophisticated analysis of medical texts. GPT-4's advanced understanding of context and semantics [3, 4, 5] offers an opportunity to transcend the boundaries of traditional lexical analysis, enabling a deeper, more meaningful comparison of medical documents [6, 7]. This approach not only addresses the shortcomings of ROUGE and BLEU but also aligns with the evolving needs of medical data analysis, where the accurate interpretation of texts is preeminent.