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 paper introduces MIMIC-IV-Phenotype-Atlas (MIPA), the first publicly available benchmark dataset featuring expert-annotated discharge summaries across 16 phenotypes, which enables standardized evaluation of phenotyping methods and demonstrates that large language models outperform traditional rule-based and machine learning approaches in identifying complex medical conditions.
This qualitative study of stakeholders in Uganda highlights that while mobile phone-based tools offer significant potential for improving non-communicable disease surveillance in low-resource settings, their successful and responsible implementation depends on proactively addressing critical ethical, legal, and social challenges related to privacy, equity, and data governance.
This bibliometric review of 1,933 documents reveals a critical gap in combining machine learning, explainable AI, and knowledge graphs for Type 2 diabetes risk prediction, proposing a new framework to bridge statistical explanations with structured clinical pathways for improved clinical decision support.
This study demonstrates that Decision Curve Analysis provides a superior framework for evaluating machine learning models predicting next-day ICU transfers by quantifying clinical decision utility and optimizing operational thresholds to align with real-world workflow constraints, outperforming traditional discrimination metrics and simple clinical rules.
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.