health informatics 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.

📄 health informatics

Attitudes and Perceptions of Generative Artificial Intelligence Chatbots in the Scientific Process of Traditional, Complementary, and Integrative Medicine Research: A Large-Scale, International Cross-Sectional Survey

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.

Ng, J. Y., Tan, J., Syed, N., Adapa, K., Gupta, P. K., Li, S., Mehta, D., Ring, M., Shridhar, M., Souza, J. P., Yoshino (…)2026-04-15

📄 health informatics

A case report on gendered biases in a Finnish healthcare AI assistant

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.

Luisto, R., Snell, K., Vartiainen, V., Sanmark, E., Äyrämö, S.2026-04-14

📄 health informatics

Nationwide Prediction of Missed and Cancelled Appointments Using Real-World EHR Data

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.

Miran, S. A., Cheng, Y., Faselis, C., Brandt, C., Vasaitis, S., Nesbitt, L., Zanin, L., Tekle, S., Ahmed, A., Nelson, S. (…)2026-04-13

📄 health informatics

Spine Reviews: Crowdsourcing Global Spine Expert Knowledge via Digital Ledger Technology

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.

Challier, V., Diebo, B., Lafage, V., Dehouche, N., Lonjon, G., Cristini, J., SpineDAO,2026-04-13

📄 health informatics

Individualised evoked response detection based on the spectral noise colour

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.

Undurraga Lucero, J. A., Chesnaye, M., Simpson, D., Laugesen, S.2026-04-13

📄 health informatics

Early Detection of Absurdity Signals in Pharmacovigilance: A Machine Learning Ensemble Approach to Identify Rare Adverse Drug Reactions

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.

Dasgupta, R.2026-04-12

📄 health informatics

Validated Synthetic Data Generation from a Multicenter Spine Surgery Registry: Methodology and Benchmark

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.

Challier, V., Jacquemin, C., Diebo, B., Dehouche, N., Denisov, A., Cristini, J., Campana, M., Castelain, J.-E., Lonjon (…)2026-04-11

📄 health informatics

Spatial Decomposition of Longitudinal RNFL Maps Reveals Distinct Modes of Glaucomatous Progression with Structure Function and Genetic Signatures

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.

Chen, L., Zhao, Y., Moradi, M., Eslami, M., Wang, M., Elze, T., Zebardast, N.2026-04-11

📄 health informatics

An End-to-End Synthetic Oncology Clinical Trial Framework Integrating Radiographic Response, Circulating Tumor DNA, Safety, and Survival for Decision-Oriented Clinical Data Science

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.

Petalcorin, M. I. R.2026-04-08

📄 health informatics

Using Relative Risk Rankings to Understand Information Differences in Multimodal Prediction Models

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.

Kim, C., Yoon, W., Lee, H., Lee, J.-O., Afshar, M., Kang, J., Miller, T. A.2026-04-07

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