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.

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📄 health informatics

Who is leading medical AI? A systematic review and scientometric analysis of chest x-ray research

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.

Vasquez-Venegas, C., Chewcharat, A., Kimera, R., Kurtzman, N., Leite, M., Woite, N. L., Muppidi, I. J., Muppidi, R. J., Liu, X., Ong, E. P., Pal, R., Myers, C., Salzman, S., Patscheider, J. S., John (…)2026-04-07📄 health informatics

Attitudes and Perceptions Toward the Use of Artificial Intelligence Chatbots for Peer Review in Medical Journals: A Large-Scale, International Cross-Sectional Survey

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.

Ng, J. Y., Bhavsar, D., Dhanvanthry, N., Bouter, L., Chan, T., Cramer, H., Flanagin, A., Iorio, A., Lokker, C., Maisonneuve, H., Marusic, A., Moher, D.2026-04-07📄 health informatics

Perioperative Mortality Prediction Using a Bayesian Ensemble with Prevalence-Adaptive Gating

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.

Pandey, A. K.2026-04-06📄 health informatics

Citation Hallucination Determines Success: An Empirical Comparison of Six Medical AI Research Systems

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.

Shi, X., Tian, Z., Tan, S., Wang, X.2026-04-04📄 health informatics

Multi-Task Learning and Soft-Label Supervision for Psychosocial Burden Profiling in Cancer Peer-Support Text

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.

Wang, Z., Cao, Y., Shen, X., Ding, Z., Liu, Y., Zhang, Y.2026-04-04📄 health informatics

A Reproducible Health Informatics Pipeline for Simulating and Integrating Early-Phase Oncology Clinical, Biomarker, and Pharmacokinetic Data for Exploratory Decision-Support Analytics

This paper presents a reproducible Python-based workflow that simulates and integrates early-phase oncology clinical, biomarker, and pharmacokinetic data to generate analysis-ready datasets, visualizations, and exploratory predictive models for translational decision support.

Petalcorin, M. I. R.2026-04-02📄 health informatics

Development and Temporal Evaluation of Multimodal Machine Learning Models to Predict High Inpatient Opioid Exposure

This study demonstrates that multimodal machine learning models integrating structured electronic health record data with clinical note embeddings can accurately predict high inpatient opioid exposure, thereby supporting targeted opioid stewardship efforts.

Kale, S., Singh, D., Truumees, E., Geck, M., Stokes, J.2026-04-02📄 health informatics

Self-Reported Symptoms Enable Four-Phase Menstrual Cycle Classification with Hormonally Validated Labels

This study demonstrates that a hybrid machine learning framework combining gradient boosting and Hidden Semi-Markov Models can accurately classify four menstrual cycle phases using only self-reported symptoms, achieving 67.6% accuracy and establishing symptom dynamics as a scalable, device-free digital biomarker for reproductive health.

Specht, B., Tayeb, Z. Z., Garbaya, S., Khadraoui, D., EL-Khozondar, M., Schneider, R.2026-04-01📄 health informatics

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