📄 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

Comparing Human and Large Language Model Responses to Patients Online Questions: Towards Multi-dimensional Patient-centered Support

This study empirically compares large language models and peer responses to patients' online questions about laboratory test results, finding that while LLMs excel at providing clear, structured medical explanations, peers offer more personalized emotional support, suggesting that LLMs could effectively complement peer communities if they improve their emotional depth, reasoning transparency, and alignment with community norms.

Hussein, M. A., Doshi, R., He, L., Reynolds, T.2026-07-17
📄 health informatics

FootNet: A Multi-View Smartphone Dataset and Four-Model Benchmark for Clinical Foot Segmentation

This paper introduces FootNet, a multi-view smartphone dataset with expert-annotated masks for clinical foot segmentation, and establishes a benchmark demonstrating that a U-Net with a MobileNetV2 encoder significantly outperforms other models, including DeepLabV3, UNet++, and SAM ViT-B, in terms of segmentation accuracy.

Vijay, A., Prabhune, A., Srihari, V. R., Rayampalli, A.2026-07-17
📄 health informatics

Beyond Intensity: Cross-Dataset Consistency of Temporal Facial Action-Unit Dynamics as Transferable Markers of Depression

This study demonstrates that while high-intensity facial action unit features often fail to generalize across diverse datasets, slower temporal dynamics and specific co-activation patterns (such as eye-mouth decoupling) serve as robust, transferable markers of depression, suggesting that directional consistency is a superior criterion for selecting features in multi-site affective research.

Jeong, I., Jang, M., Kim, J.-w., Kim, H., Park, S., Kim, D.-K., Park, J.-H., Kim, Y., Kim, J.-M., Lee, H., Jhon, M.2026-07-15
📄 health informatics

How Best to Explain Machine Learning Models to Clinicians: A User Study of Explanation Types

This user study involving 39 clinicians demonstrates that while attribution-based explanations most significantly enhance trust and understanding of machine learning predictions in clinical settings, nearly half of the participants prefer viewing multiple explanation types, suggesting that future implementations should prioritize attribution methods while offering diverse formats tailored to specific clinical roles.

Brown, B., Oguss, M., Carey, K. A., Martin, J., Kotula, C. A., Nguyen, O. T., Akel, M., Wiegmann, D. A., Edelson, D. P. (…)2026-07-10
📄 health informatics

Adaptation and Psychometric Validation of a Facility-Level Tool to Assess Telemedicine Readiness in Primary Care

This study successfully adapted and psychometrically validated the Telemedicine Readiness Inventory at the Facility Level (TRI-F) using data from 774 primary care facilities in Peru, demonstrating its structural validity, internal consistency, and utility for benchmarking and guiding telemedicine implementation planning.

Escobar-Agreda, S., Villarreal-Zegarra, D., Reategui-Rivera, C. M., Paredes-Gonzales, Y., Rojas-Mezarina, L.2026-07-10
📄 health informatics

Does OMOP CDM Conversion Improve Cross-Country Comparability of Real-World Data? A Benchmark Study in Breast Cancer and Amyotrophic Lateral Sclerosis

This benchmark study demonstrates that while converting real-world data from Denmark, Finland, and Portugal to the OMOP Common Data Model enables semantically aligned cross-country comparisons for breast cancer and ALS, it does not eliminate underlying data heterogeneity, necessitating iterative benchmarking against native data and clinical expertise to ensure valid epidemiological insights.

Aborageh, M., Korcinska Handest, M. R., Bakos, I., Rajamaki, B., Silva, C., Horvath-Puho, E., Pylkkaenen, L., Venda, C. (…)2026-07-09
📄 health informatics

A large language model-assisted workflow for generating a living evidence base for climate-sensitive foodborne disease

This study demonstrates that an LLM-assisted workflow, combining structured searches with iterative GPT-4-Turbo refinement, can effectively generate a rapid, scalable, and policy-relevant living evidence base for climate-sensitive foodborne diseases with high recall and improved screening consistency.

Elson, R., McIntyre, K. M., Hardingham, M. B., Luechtefeld, T., Lake, I. R.2026-07-08
📄 health informatics

RenalTransLSTM: Multi-Horizon Prediction of Acute Kidney Injury in ICU Patients using a Hybrid LSTM-Transformer Architecture

RenalTransLSTM is a hybrid deep learning framework that combines LSTM and Transformer architectures to achieve superior multi-horizon prediction of acute kidney injury in ICU patients by effectively capturing both local temporal dynamics and global contextual dependencies in electronic health records.

Badhon, S. M. S. I., Adibuzzaman, M., Mosa, A. S. M., Bozdag, S., Cleveland, A. D., Ding, J., Hossain, K. S. M. T.2026-07-06