127 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 presents a governed Bayesian digital twin for Parkinson's disease that integrates sequential updating with a confidence-based suppression mechanism to deliver calibrated, equitable, and auditable multi-domain progression forecasts while explicitly withholding unreliable predictions.
This study demonstrates that aggregate benchmark scores fail to capture critical patient safety risks in frontier language models for healthcare, as significant and unpredictable variations in error directionality, contextual bias, and crisis response across models reveal that overall accuracy alone cannot predict clinical safety.
This study demonstrates that combining deep learning with Chi-square feature selection and Synthetic Minority Over-sampling (SMOTE) techniques significantly improves Medicare fraud detection accuracy to 95.4% by effectively addressing dataset imbalance and irrelevant features.
This prospective cohort study of 2,847 Canadian IBS patients utilizes joint longitudinal-survival modeling to demonstrate that individual symptom trajectories are dynamically linked to treatment discontinuation, revealing that higher baseline severity and slower rates of symptom improvement significantly increase the risk of stopping therapy.
This paper introduces HybridNet-XR, a memory-efficient, teacher-free self-supervised hybrid CNN that achieves state-of-the-art diagnostic accuracy on chest radiographs with minimal VRAM usage, offering a robust solution for autonomous medical systems in resource-constrained environments.
This qualitative study of 28 clinicians at Singapore's Alexandra Hospital reveals that while ambient AI scribe technology offers significant potential to reduce administrative burden and enhance patient engagement, its successful implementation in Singapore's multilingual healthcare system requires addressing critical challenges related to documentation accuracy, workflow adaptation, and compliance with local privacy regulations.
The paper introduces OpenScientist, an open-source agentic AI co-scientist that significantly accelerates biomedical discovery by autonomously executing complex data analyses and generating verifiable clinical insights across diverse case studies, reducing tasks that typically take weeks to mere minutes.
This pre-registered falsification study across four clinical datasets demonstrates that while sepsis prediction models at elite academic centers primarily detect genuine biological signals rather than care-process intensity, they reveal a systematic and consequential divergence between clinical sepsis definitions and administrative coding that undermines the validity of regulatory metrics and AI benchmarks relying on the latter.
This national fairness audit of 386,775 US adults demonstrates that enforcing demographic parity in HIV testing prediction models is inappropriate for differential-burden clinical contexts, as it significantly reduces screening access for high-risk populations and underscores the need for fairness metrics like equalized odds and calibration that align with clinical needs.
This paper, titled "Determinants of Digital Health Technology Acceptance Among Healthcare Caregivers: A Structural Equation Modeling Approach," has been withdrawn from medRxiv due to the submission of false information.