In the 21st century, the proliferation of fake information has emerged as a significant threat to society. Particularly, healthcare medical reporters face challenges when verifying claims related to treatment effects, side effects, and risks mentioned in news articles, relying on scientific publications for accuracy. The accurate communication of scientific information in news articles has long been a crucial concern in the scientific community, as the dissemination of misinformation can have dire consequences in the healthcare domain. Healthcare medical reporters would greatly benefit from efficient methods to retrieve evidence from scientific publications supporting specific claims. This paper delves into the application of unsupervised semantic similarity models to facilitate claim verification for medical reporters, thereby expediting the process. We explore unsupervised multilingual evidence retrieval techniques aimed at reducing the time required to obtain evidence from scientific studies. Instead of employing content classification, we propose an approach that retrieves relevant evidence from scientific publications for claim verification within the healthcare domain. Given a claim and a set of scientific publications, our system generates a list of the most similar paragraphs containing supporting evidence. Furthermore, we evaluate the performance of state-of-the-art unsupervised semantic similarity methods in this task. As the claim and evidence are present in a cross-lingual space, we find that the XML-RoBERTa model exhibits high accuracy in achieving our objective. Through this research, we contribute to enhancing the efficiency and reliability of claim verification for healthcare medical reporters, enabling them to accurately source evidence from scientific publications in a timely manner.