Healthcare professionals are often overworked, which may impair their efficacy.Text search engines may facilitate their work.However, before making health decisions, it is important for a medical professional to consult verified sources rather than unknown web pages.In this work, we present our approach for creating a text search engine based on verified resources in the Polish language, dedicated to medical workers.This consists of collecting and comprehensively analyzing texts annotated by medical professionals and evaluating various neural reranking models.During the annotation process, we differentiate between an abstract information need and a search query.Our study shows that even within a group of trained medical specialists there is extensive disagreement on the relevance of a document to the information need.We prove that available multilingual rerankers trained in the zero-shot setup are effective for the Polish language in searches initiated by both natural language expressions and keyword search queries.

Multimodal models, which combine visual and textual information, have recently gained significant recognition.This paper addresses the multimodal challenge of Text-Image retrieval and introduces a novel task that extends the modalities to include temporal data.The Temporal Image Caption Retrieval Competition (TICRC) presented in this paper is based on the Chronicling America and Challenging America projects, which offer access to an extensive collection of digitized historic American newspapers spanning 274 years.In addition to the competition results, we provide an analysis of the delivered dataset and the process of its creation.