arXiv:2607. 26700v1 Announce Type: cross Abstract: The Mpox outbreak remains a serious public health issue, with the WHO (World Health Organization) reporting increasing cases in some regions.
arXiv:2606. 19345v1 Announce Type: cross Abstract: The rapid increase in scientific publications leads to the fact that manual study screening in systematic literature reviews (SLRs) is increasingly resource consuming, inefficient, and inconsistent.
arXiv:2603. 22327v2 Announce Type: replace-cross Abstract: Systematic literature reviews (SLRs) are a demanding and high-stakes form of scientific knowledge synthesis that remains underspecified as an evaluation setting for large language models (LLMs).
arXiv:2608. 15691v1 Announce Type: cross Abstract: Health misinformation circulating during pandemics can gain traction rapidly, creating harmful narratives that compete with public health guidance.
arXiv:2410. 15051v3 Announce Type: replace-cross Abstract: Identifying patient diagnoses from hospital discharge letters is essential for large-scale cohort selection and epidemiological research, but traditional supervised approaches require extensive manual annotation, which is often impractical for large textual datasets.
arXiv:2607. 08299v2 Announce Type: replace Abstract: Diagnostic decision making often relies on a sequence of pathology tests that bridge patient symptoms and final disease diagnosis.
arXiv:2606. 27174v1 Announce Type: new Abstract: Medical device recalls are a critical regulatory mechanism for protecting patient safety.
Medical device recalls are a critical regulatory mechanism for protecting patient safety. The growing volume of FDA recall records presents challenges in post-report recall triage, severity assessment, and root-cause interpretation.
arXiv:2512. 01241v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.
arXiv:2606. 08376v1 Announce Type: cross Abstract: As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and diversity.
The rapid spread of false and misleading health information through digital platforms has become a major public health challenge, particularly during infectious disease outbreaks where delayed verification can influence public behaviour and hinder effective disease control. Although recent advances in automated health misinformation detection have shown encouraging results, most existing approaches rely heavily on global biomedical resources and often fail to capture the local context needed to verify claims in developing countries.
arXiv:2606. 19852v1 Announce Type: cross Abstract: Information extraction from pathology reports is essential for cancer staging, tumor registry population.
arXiv:2607. 00472v1 Announce Type: cross Abstract: Cardiovascular disease is still one of the main causes of death around the world.