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.
A usability assessment of Trialshub, an LLM-powered chat platform for clinical trial matching, found that while its intuitive design and workflow efficiency are highly valued by users, addressing technical reliability issues and refining conversational memory are critical for successful real-world deployment.
This study demonstrates that a pretrained Wav2Vec 2.0 model can robustly estimate gender and preserve age-related patterns in pathological speech across multilingual datasets, achieving high accuracy for gender and significant age correlations in connected speech while revealing task-dependent limitations for sustained vowel phonation.
This large-scale international survey reveals that while a majority of Traditional, Complementary, and Integrative Medicine (TCIM) researchers recognize the potential of Generative AI chatbots to enhance research efficiency and reduce workload, they also identify significant challenges regarding bias and errors, highlighting a critical need for institutional training programs to support their effective integration.
This study reveals that a Finnish healthcare AI assistant exhibits significant gendered biases, including stereotypical framing of female patients around reproductive health and inconsistent clinical reasoning, stemming from flaws in both its retrieval and generation stages.
This retrospective study utilizing a large national EHR dataset demonstrates that machine learning models, particularly XGBoost, can accurately predict unused outpatient appointments with an AUC of 0.95, suggesting that integrating such predictive tools into scheduling workflows could significantly improve healthcare efficiency.
This prospective study demonstrates that a novel blockchain-based crowdsourcing platform using Soulbound Tokens successfully aggregated rapid, cost-effective, and clinically coherent treatment recommendations from a global panel of spine specialists, revealing significant inter-clinician variability and establishing the need for diverse, multi-reviewer data in predictive modeling.
The paper introduces Fmpi, a computationally efficient framework for individualised, real-time detection of evoked potentials that models subject-specific noise characteristics to achieve superior specificity and enable early termination of uninformative recordings compared to existing methods.
This paper proposes a machine learning ensemble framework that preserves and analyzes rare adverse events as "absurdity signals" rather than discarding them as outliers, successfully identifying critical safety patterns in Losartan data that traditional biostatistical methods would miss.
This study presents and validates a three-domain framework for generating blockchain-anchored synthetic spine surgery data from a multicenter registry, demonstrating that the resulting datasets successfully balance patient privacy, statistical fidelity, and utility for AI development.
This study demonstrates that spatially decomposing longitudinal retinal nerve fiber layer maps reveals six distinct modes of glaucomatous progression that outperform conventional global averaging in predicting visual field decline and capturing stronger genetic associations, thereby uncovering biologically homogeneous endophenotypes masked by traditional methods.