225 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 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.
This study presents a comprehensive, literature-informed synthetic phase II oncology clinical trial framework that successfully integrates radiographic, molecular (ctDNA), safety, and survival data to generate a biologically plausible, analytically coherent efficacy-safety signal, thereby serving as a decision-oriented prototype for translational clinical data science.
This study demonstrates that replacing raw chest radiographs with expert-written reports in multimodal mortality prediction models leads to significant information loss and altered risk prioritization, suggesting that text summaries are imperfect proxies for visual prognostic cues.
This systematic review and scientometric analysis of 928 chest X-ray AI studies reveals that research leadership and training data are overwhelmingly dominated by high-income countries, particularly the US and China, creating significant disparities that risk developing AI systems with inconsistent performance across diverse global populations and exacerbating healthcare inequities.
This large-scale international survey reveals that while medical journal peer reviewers are highly familiar with AI chatbots, their actual use in peer review remains limited due to significant ethical concerns and a lack of institutional training, despite a strong expressed interest in receiving guidance for future implementation.
This study presents a prevalence-adaptive Bayesian ensemble model that utilizes Variational Autoencoder-based data augmentation and entropy-driven uncertainty quantification to achieve perfect separation on a validation cohort and clinically meaningful sensitivity with zero false positives in predicting perioperative mortality within resource-limited surgical settings.
This paper introduces MedResearchBench to evaluate six medical AI research systems, revealing that citation hallucination is the decisive factor in their success and demonstrating that multi-agent verification pipelines significantly outperform subjective single-model evaluations in ensuring trustworthy scientific writing.
This study demonstrates that composite-only multi-task learning effectively profiles multidimensional psychosocial burdens in cancer peer-support text, while soft-label supervision derived from large language models underperforms compared to hard-label baselines for emotion classification.