Multimodal Learning for Supporting Evidence Base Medicine

Practicing evidence-based medicine relies on using a combination of various devices and technologies to support clinical decision-support. This multimodal approch improve prediction by fusing disparate data to better meet the precise diagnosis of diseases, such as medical images and histological information which can reflect the condition of the patient more comprehensively. Multimodal learning, however, utilizes techniques to process and find relationships between different types of data modalities to comprehend the case description as some clinical cues exist only in certain modalities. Actually in clinical practice, clinicians use imaging modalities like computed tomography (CT) scans or X-ray images beside non-image modalities like electroencephalogram (EEG) data or tabular data used in the electronic healthcare records to describe clinical cases. In this article we are presenting a multimodal machine framework that can be used for evidence-based practice using fine tuned MedCLIP transformer model to fetch relevant PubMed articles based on three different scenarios of usage where clinicians can provide multimodal queries in their search for relevant PubMed articles.