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 study demonstrates that few-shot prompted large language models can effectively categorize specific types of clinician edits to ambient AI draft notes, achieving strong performance for medication and symptom modifications while identifying complex, context-dependent edits as better suited for human review triage.
The 2025 review of healthcare AI research reveals a near-doubling of publication volume and a significant maturation of the field, characterized by a shift from classical machine learning and text-only models toward the rapid adoption of multimodal foundation models that better reflect the complexity of real-world clinical practice.
This cohort study found that the type of editorial response (retraction, expression of concern, or editorial notice) issued for integrity concerns in randomized controlled trials did not significantly influence subsequent citation rates or accelerate citation decline compared to control trials.
This study presents a governance-driven health informatics framework that leverages real-world longitudinal data to model patient treatment sequencing, persistence, and provider adoption, thereby significantly improving the accuracy of healthcare utilization forecasts for oncology and complex chronic conditions compared to traditional static market-share approaches.
This study evaluates the representational robustness of 15 locally deployable large language models on neurological terminology, revealing that while performance generally scales with model size, neither size nor medical fine-tuning guarantees clinical reliability due to significant variations based on terminological complexity and subdomain.
This study demonstrates that a Nash bargaining-based multi-agent language model framework significantly improves the safety and efficiency of care plans for underserved Medicaid patients compared to a single-model baseline, while highlighting that equity requires explicit design attention rather than emerging automatically from multi-objective optimization.
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.