225 papers
Health informatics sits at the vibrant intersection of medicine, data science, and technology, transforming how we store, analyze, and utilize health information. This rapidly evolving field empowers clinicians and researchers to uncover patterns in patient data, improve diagnostic accuracy, and personalize treatment plans without getting lost in complex databases. By turning raw medical records into actionable insights, these innovations are reshaping the future of healthcare delivery and population health management.
At Gist.Science, we bridge the gap between cutting-edge research and public understanding by curating the latest preprints from medRxiv specifically within this domain. Our team processes every new submission in this category, providing both accessible plain-language explanations and detailed technical summaries to ensure the science is clear for everyone, from policymakers to curious readers. Below are the latest papers in health informatics, freshly distilled and ready for you to explore.
Genosolver is an integrated workflow that leverages Large Language and Reasoning Models to extract detailed clinical insights from unstructured narratives, significantly improving rare disease diagnosis rates and outperforming existing tools like Exomiser by effectively prioritizing causative genetic variants.
This paper presents a methodological framework for localizing the open-source medical imaging software 3D Slicer into Brazilian Portuguese, addressing specific linguistic and terminological challenges to enhance accessibility for non-English-speaking users in Latin America.
This paper proposes K-NeSyNet, a novel knowledge-driven neuro-symbolic framework that integrates a multi-modal oncology knowledge graph with a differentiable three-channel symbolic reasoning mechanism and adaptive neural fusion to deliver accurate, safe, and interpretable personalized cancer treatment recommendations.
This paper empirically demonstrates that relying solely on identifier matching for biomedical knowledge graph integration is insufficient, revealing that while cross-ontology and embedding-based methods increase coverage, they systematically introduce clinically significant failure modes like over-merging and semantic collapse that obscure critical distinctions in downstream applications.
This paper introduces an Exploratory AI Recommender that leverages explainable AI to generate data-driven recommendations for feature selection, non-linear terms, and interactions, thereby significantly enhancing the predictive performance and interpretability of high-dimensional clinical models like the Cox Proportional Hazards model.
This study evaluates an ambient AI documentation system's performance in mixed-language clinical encounters, finding that while overall transcription error rates are low and language switching is generally detected reliably, significant challenges remain with Mandarin-English code-switching, including high error outliers and frequent deletions at switch points.
This study evaluates frontier large language models for translating multimodal clinical phenotype documentation into executable EHR algorithms, finding that while they effectively interpret structured text, their performance significantly degrades with diagram-only inputs, ultimately identifying documentation quality rather than model capability as the primary bottleneck.
This systematic review and meta-analysis of 30 studies demonstrates that machine learning and deep learning algorithms achieve high diagnostic accuracy for early Alzheimer's disease detection, though the field requires standardized evaluation protocols and external validation to mitigate overfitting and ensure clinical viability.
This study reveals that multilingual consumer health AI exhibits a critical asymmetry where clinical substance and safety vary significantly by language—often failing silently in non-English contexts—while maintaining a consistent, empathetic tone across all languages.
This paper proposes an epidemiology-informed graph neural network (EIGNN) framework that integrates mechanistic epidemiological models with data-driven contact networks to accurately predict and interpret hospital-acquired infection dynamics while ensuring clinical trust through transparency.