256 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.
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.
This cross-sectional analysis of 1,430 FDA authorizations from 1995 to 2025 reveals that while AI/ML-enabled medical device approvals have surged exponentially, they remain heavily concentrated in image-rich diagnostic specialties like radiology, leaving significant gaps in representation for other major clinical fields such as pathology, obstetrics, and behavioral health.
This study develops and validates an interpretable machine learning framework using Extreme Gradient Boosting to accurately classify malaria transmission states across Kenya's 47 counties from 2015 to 2025, demonstrating that integrating epidemiological and environmental data can effectively support targeted surveillance and resource allocation.
The paper introduces MISP-Bench, a large-scale factorial benchmark evaluating how open-weight language models respond to user-provided false priors in clinical and educational contexts, revealing that combined answer-rationale attacks exhibit sub-additive damage, that targeted distractors significantly increase sycophancy compared to arbitrary ones, and that specific safety guard strategies (like source-independence and explicit overrides) effectively mitigate misinformation susceptibility across diverse models.
This paper introduces the Missingness Demographic Leakage Audit (MDLA), a reproducible framework that reveals how patterns of missing clinical data in critical care mortality models can act as subtle, unmeasured demographic proxies, necessitating the integration of missingness-aware auditing and calibration-aware evaluation into clinical AI validation pipelines.
This comparative evaluation demonstrates that while fine-tuned generalist large language models generally underperform specialized clinical foundation models on structured EHR disease risk prediction, LLM-generated embeddings paired with lightweight classifiers can achieve superior performance across both AUROC and AUPRC metrics.
The REVELIO protocol outlines a randomized, controlled pilot study on a closed test track designed to evaluate the feasibility of a multimodal in-vehicle system for detecting cannabis-impaired driving in healthy recreational users by correlating vehicle, driver, and biological data following controlled THC administration.
The paper introduces AERO, an AI agent framework that optimizes clinical trial eligibility criteria for real-world data emulation by leveraging large language models to systematically classify and refine criteria, thereby improving the generalizability and accuracy of treatment effect estimates as demonstrated in a WARCEF trial emulation.
This paper introduces EquiLense, a practical, post-hoc, and model-agnostic auditing tool that bridges the gap between group and individual fairness assessments in clinical AI by utilizing a novel metric called Mean Predicted Probability Difference (MPPD) to identify systematic prediction inconsistencies across demographic groups.
MedAdhereAI is an interpretable machine learning pipeline that utilizes real-world refill and claims data to predict medication non-adherence in chronic disease patients, providing actionable insights through SHAP explainability to support targeted clinical interventions.