📄 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

Perceptions of Artificial Intelligence in the Editorial and Peer Review Process: A Cross-Sectional Survey of Traditional, Complementary, and Integrative Medicine Journal Editors

A cross-sectional survey of Traditional, Complementary, and Integrative Medicine journal editors reveals that while they recognize the potential of artificial intelligence to support routine editorial tasks, its actual adoption remains limited due to a lack of institutional policies, training, and ethical guidance.

Ng, J. Y., Bhavsar, D., Krishnamurthy, M., Dhanvanthry, N., Fry, D., Kim, J. W., King, A., Lai, J., Makwanda, A., Olugbe (…)2026-03-04
📄 health informatics

Understanding Clinician Edits to Ambient AI Draft Notes: A Feasibility Analysis Using Large Language Models

This study demonstrates that few-shot prompted large language models can effectively categorize specific types of clinician edits to ambient AI draft notes, achieving strong performance for medication and symptom modifications while identifying complex, context-dependent edits as better suited for human review triage.

Guo, Y., Zhou, Y., Hu, D., Sutari, S., Chow, E., Tam, S., Perret, D., Pandita, D., Zheng, K.2026-03-02
📄 health informatics

Artificial Intelligence in Healthcare: 2025 Year in Review

The 2025 review of healthcare AI research reveals a near-doubling of publication volume and a significant maturation of the field, characterized by a shift from classical machine learning and text-only models toward the rapid adoption of multimodal foundation models that better reflect the complexity of real-world clinical practice.

Edara, R., Khare, A., Atreja, A., Awasthi, R., Highum, B., Hakimzadeh, N., Ramachandran, S. P., Mishra, S., Mahapatra, D (…)2026-02-28
📄 health informatics

A Governance-Driven, Real-World Data-Calibrated Health Informatics Framework for Longitudinal Utilization Forecasting in Oncology and Complex Chronic Conditions

This study presents a governance-driven health informatics framework that leverages real-world longitudinal data to model patient treatment sequencing, persistence, and provider adoption, thereby significantly improving the accuracy of healthcare utilization forecasts for oncology and complex chronic conditions compared to traditional static market-share approaches.

Dantuluri, A. V. S. R., Kumar, S.2026-02-26
📄 health informatics

On the robustness of medical term representations in locally deployable language models

This study evaluates the representational robustness of 15 locally deployable large language models on neurological terminology, revealing that while performance generally scales with model size, neither size nor medical fine-tuning guarantees clinical reliability due to significant variations based on terminological complexity and subdomain.

Auger, S. D., Graham, N. S. N., Scott, G.2026-02-26
📄 health informatics

Care Plan Generation for Underserved Patients Using Multi-Agent Language Models: Applying Nash Game Theory to Optimize Multiple Objectives

This study demonstrates that a Nash bargaining-based multi-agent language model framework significantly improves the safety and efficiency of care plans for underserved Medicaid patients compared to a single-model baseline, while highlighting that equity requires explicit design attention rather than emerging automatically from multi-objective optimization.

Basu, S., Baum, A.2026-02-25