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.
The AutoscriberValidate study demonstrates that using the ambient digital scribe Autoscriber significantly reduces healthcare providers' typing workload and improves the overall quality of electronic medical notes by decreasing various types of documentation errors.
A cross-sectional survey of 500 patients in Jordan reveals that while there is conditional optimism regarding AI in healthcare, acceptance is heavily dependent on maintaining human physician involvement, ensuring transparency and privacy, and addressing disparities linked to education and digital literacy.
This study develops and validates a novel hybrid SARIMA-CNNAR model using ten years of national surveillance data to accurately forecast monthly tuberculosis incidence in Nepal, demonstrating superior performance over standalone statistical and machine learning benchmarks to support evidence-based public health planning.
MedOS is a general-purpose embodied world model that bridges the gap between abstract clinical reasoning and physical intervention by utilizing a dual-system architecture to autonomously execute complex medical procedures, simulate adverse events, and democratize clinical expertise by narrowing the performance gap between junior and senior physicians.
This study demonstrates that a fully automated pipeline using large language models can generate systematic reviews with citation accuracy and expert-rated quality surpassing human-authored counterparts, while simultaneously revealing critical limitations in information breadth and the urgent need for new verification standards and AI literacy in scientific publishing.
This study utilizes a graph neural network-based sentiment analysis model on over 340,000 app store reviews to identify that while therapeutic features like sound masking and sleep support drive positive user sentiment, issues regarding pricing, advertisements, and technical stability remain key areas for improvement in tinnitus management apps.
This study of 30 clinicians reveals that edits to ambient AI-drafted clinical notes are primarily driven by the need to correct transcription errors, ensure clinical accuracy, mitigate liability risks, and meet billing standards, highlighting the necessity for improved AI customization, integration, and institutional support to enhance human-AI collaboration.
This paper introduces CardioPulmoNet, a physiologically inspired neural network architecture that models cardiopulmonary dynamics to achieve stable, interpretable, and high-performing histopathological diagnosis, particularly under limited data conditions.
This study validates "otto-SR," a large language model-based workflow that demonstrates high performance in automating article screening, data extraction, and risk of bias assessment, thereby enabling the rapid and reliable reproduction and updating of systematic reviews.
The paper presents Lilo Engine, a deterministic 5-layer therapeutic AI pipeline for elderly assisted living that replaces unreliable multi-agent systems with structural safety invariants to achieve 100% crisis recall, sub-30ms latency, and fully auditable execution paths across 3,720 test scenarios.