The rapid dissemination of health information online enables dangerous distortions that threaten public health. We present$\text{HD}^{2} \text{CR}$(health-information distortion detection with contrastive reasoning), a framework for fine-grained detection of medical misinformation. Through systematic analysis of health news patterns, we identify four primary distortion types: over-generalization, exaggeration, under-generalization, and false causality. Our contributions include: a cross-lingual corpus of 72,275 English and Chinese claim-evidence pairs with validated distortion labels; a dual-encoder architecture with contrastive cross-attention that explicitly models semantic divergence between claims and biomedical evidence; and extensive evaluations demonstrating$\text{HD}^{2}{ }^{2} \text{CR}^{\prime}$s superior performance: 93.1% binary$F_{1}$and$87.3 \% 5$-class accuracy, with robust cross-lingual generalization (only 2.9% degradation between Chinese and English).

Verifying health claims amid rampant misinformation requires identifying qualified experts - a manual process that cannot scale. We address this challenge with a computational framework that automatically identifies biomedical researchers to evaluate health claims by analyzing their PubMed publication profiles. We establish the first benchmark for this cross-genre retrieval task, linking 93,404 health claims to 153,147 biomedical experts. Our two-stage neural pipeline addresses the semantic heterogeneity between informal health claims and formal research literature. Systematic evaluation reveals a striking finding: domain-specific models achieve 84.2% Mean Reciprocal Rank, substantially outperforming general-purpose alternatives, including state-of-the-art LLM-based rerankers fine-tuned on domain-specific data. Our findings underscore the necessity of specialized benchmarks for cross-genre information retrieval, and specialized pretraining for biomedical expert identification, while our scalable architecture enables rapid, automated expert matching for evidence-based claim verification in clinical and public health contexts.

We present a query-based biomedical information retrieval task across two vastly different genres -newswire and research literaturewhere the goal is to find the research publication that supports the primary claim made in a health-related news article.For this task, we present a new dataset of 5,034 claims from news paired with research abstracts.Our approach consists of two steps: (i) selecting the most relevant candidates from a collection of 222k research abstracts, and (ii) re-ranking this list.We compare the classical IR approach using BM25 with more recent transformerbased models.Our results show that crossgenre medical IR is a viable task, but incorporating domain-specific knowledge is crucial.