📄 Category

health informatics

180 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

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
📄 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