📄 Category

health informatics

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.

📄 health informatics

Supervised Contrastive Learning-based Digital Biomarker Discovery for Wearable IMU Gait Signals

This study introduces the Embedding-Distance Gait Biomarker (EDGB), a supervised contrastive learning framework that utilizes a compact convolutional neural network to extract robust 32-dimensional latent representations from raw wearable IMU signals, achieving high accuracy in distinguishing between healthy, neurological, and orthopedic gait patterns while demonstrating strong reliability and significant group differentiation.

Mohtavipour, S. M.2026-07-04
📄 health informatics

Combining VEGFR tyrosine kinase inhibitors and PD-1/PD-L1 inhibitors versus VEGFR tyrosine kinase inhibitors monotherapy in renal cell carcinoma: a target trial emulation

This target trial emulation study using real-world data demonstrates that combining PD-1/PD-L1 inhibitors with VEGFR tyrosine kinase inhibitors significantly improves restricted mean survival time in renal cell carcinoma patients compared to VEGFR tyrosine kinase inhibitor monotherapy, supporting the generalizability of combination therapy despite non-proportional hazards.

Shi, D., Li, X., Chen, Y., Chen, Y., Song, Q., Su, J.2026-07-02
📄 health informatics

A foundation model of wearable pulse oximetry reveals physiological signatures of health and cardiometabolic risk

The paper introduces PulseOx-FM, a self-supervised foundation model trained on millions of wearable pulse oximetry segments that outperforms existing methods in predicting diverse cardiometabolic and neuropsychiatric health risks, including future hypertension and next-day glycemic states, thereby establishing a powerful non-invasive tool for global health risk stratification.

Kohn, S., Lutsker, G., Diament, A., Shilo, S., Gabet, A., Sasson, G., Wolf, G., Wolf, A., Godneva, A., Weinberger, A., R (…)2026-07-02
📄 health informatics

Evaluating Generative Video AI for Standardized Psychiatric Patient Simulation With Graded Hygiene Deterioration.

This pilot study demonstrates the technical feasibility of using generative video AI to create standardized psychiatric patient simulations with graded hygiene deterioration, while highlighting that although appearance modulation is achievable, fine-motor artifacts necessitate expert human oversight before clinical deployment.

Mwangi, B., Jabbar Abdl Sattar Hamoudi, H., Wu, M.-J., Martin, A., Soares, J. C., Soutullo, C. A.2026-06-25
📄 health informatics

Predicting Depression and Anxiety Progression in Multiple Sclerosis from Longitudinal Clinical Data Using Machine Learning

This study demonstrates that while gradient boosting models using structured electronic health record data can predict depression and anxiety progression in multiple sclerosis patients, their limited predictive power (R² ≤ 0.28) is dominated by baseline scores reflecting regression to the mean, indicating that richer data sources beyond structured clinical variables are necessary for meaningful individual-level forecasting.

Specht, B., Garbaya, S., Schneider, R., Khadraoui, D., Chavarriaga, R., Tayeb, Z.2026-06-25
📄 health informatics

Demographic Calibration Gaps in Breast Cancer Risk Prediction: Introducing the Demographic Calibration Gap Score

This paper introduces the Demographic Calibration Gap Score (DCGS) to demonstrate that standard global calibration methods fail to address systematic prediction errors across racial and gender subgroups in breast cancer risk models, particularly under distributional shifts, thereby highlighting the need for subgroup-specific calibration metrics to prevent biased clinical decisions.

Eniolade, M.2026-06-22
📄 health informatics

Generative Artificial Intelligence in Psychotherapy Practice: A Global Online Survey of Mental Health Professionals' Adoption

A global survey of 766 mental health professionals reveals that over half are already using generative AI tools, primarily for treatment planning and administrative tasks, yet this widespread adoption occurs despite a significant lack of institutional guidance, formal training, and regulatory frameworks.

Blease, C., Hagström, J., Gaab, J., Carey, A., Cipriani, F., Gorman, C., Nascimento, A. F., Fitzgerald, A., Holtz, L. (…)2026-06-22