Semantic Similarity Learning for Medical Literature: A Multimodal Framework Combining LLM and Deep Metric Learning

Deepika A^1*, Rajeswari M²

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ABSTRACT

Background: There is an increasing need for artificial intelligence in the field of medical related learning frameworks which is capable of efficiently handling the multimodal data which is under resource constrained situations. Conventional machine learning and deep learning algorithms are usually depending on the large labelled dataset and limited environment with less applicability for real world applications. The adoption of the Artificial intelligence in the field of healthcare is the need for the learning and effective handling of the multimodal healthcare data. Traditional machine learning and deep learning algorithms depends on the large-scale datasets with label, limiting the usage of the current clinical environment. This will limit the applicability of the real-world clinical environments by the data lackness and variance in the domain.

Methodology: In this work, Large Language Models are used with deep metric learning framework for multimodal medical journal classification. In this work, Large Language Model is implemented using deep metric learning framework for the multimodal dataset. The multimodal dataset comprises of text, table and images from online repository. The data manually downloaded from the PubMed website is used in this work.

Conclusion: The proposed LLM guide framework outperformed all other models trained by various benchmark datasets.

Keywords: Cross-Modal Embeddings, Deep Metric Learning, Explainable AI in Healthcare, Few-Shot Medical Classification, Large Language Models, Multimodal Learning

How to cite this article: Deepika A, Rajeswari M. Semantic Similarity Learning for Medical Literature: A Multimodal Framework Combining LLM and Deep Metric Learning. Int J Drug Deliv Technol. 2026;16(13s): 810-814. DOI: 10.25258/ijddt.16.13s.88.

Source of support: Nil.

Conflict of interest: None