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.

📄 health informatics

Agent Role Structure and Operating Characteristics in Large Language Model Clinical Classification: A Comparative Study of Specialist and Deliberative Multi-Agent Protocols

This study demonstrates that, even with fixed model parameters, altering the internal role decomposition of multi-agent LLM systems from a generic deliberative to a feature-specialist structure acts as a structured inductive bias that systematically reshapes sensitivity-specificity trade-offs and error distributions across different clinical classification tasks.

Anderson, C. G.2026-03-05

📄 health informatics

Red-Teaming Medical AI: Systematic Adversarial Evaluation of LLM Safety Guardrails in Clinical Contexts

This paper presents a systematic red-teaming framework for evaluating medical AI safety, revealing that while standard guardrails effectively block most adversarial attacks, they remain significantly vulnerable to authority impersonation strategies—particularly those framing requests as educational inquiries—which trigger behavioral mode-switching rather than factual errors.

Ekram, T. T.2026-03-05

📄 health informatics

Enhancing Prediabetes Diagnosis from Continuous Glucose Monitoring Data via Iterative Label Cleaning and Deep Learning

This paper presents a hybrid deep learning framework that combines iterative XGBoost-based label refinement with a Convolutional-Bidirectional LSTM model to significantly improve prediabetes diagnosis from Continuous Glucose Monitoring data by correcting misclassifications in the AI-READI dataset and achieving high diagnostic accuracy with reduced clinical burden.

Arethiya, N. J., Krammer, L., David, J., Bakshi, V., BasuChoudhary, A., Bhuiyan, U., Sen, S., Mazumder, R., McNeely, P.2026-03-05

📄 health informatics

Trustworthy personalized treatment selection: causal effect-trees and calibration in perioperative medicine

This paper proposes a deployment-ready framework that integrates causal inference, interpretable effect-trees, and calibration assessment to distinguish actionable treatment heterogeneity from unreliable noise, thereby enabling trustworthy personalized treatment selection in perioperative medicine.

Mittelberg, Y., Stiglitz, D. K., Kowadlo, G.2026-03-04

📄 health informatics

Evaluating a Locally Deployed 20-Billion Parameter Large Language Model for Automated Abstract Screening in Systematic Reviews

This study demonstrates that a locally deployed 20-billion parameter LLM, utilizing a sensitivity-enhanced prompting strategy, can significantly accelerate systematic review abstract screening with high accuracy and zero data privacy risks, though its performance varies by domain and is best used as a second screener alongside human experts.

Moreira Melo, P. H., Poenaru, D., Guadagno, E.2026-03-04

📄 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

Governing Trust in Health AI: A Qualitative Study of Cybersecurity Professionals Perspectives

This qualitative study reveals that cybersecurity professionals view health AI as a fragile, augmented clinical infrastructure where institutional trust is contingent upon visible governance and accountability rather than technical performance alone.

Adekunle, T., Ohaeche, J., Adekunle, T., Adekunle, D., Kogbe, M.2026-03-03

📄 health informatics

Anatomically and Biochemically Guided Deep Image Prior for Sodium MRI Denoising

This paper proposes a Deep Image Prior-based framework that integrates anatomical proton MRI and metabolic sodium MRI guidance through fused directional total variation regularization to achieve high-quality, accelerated sodium MRI reconstruction with improved structural fidelity and signal preservation.

ALI, H., Woitek, R., Trattnig, S., Zaric, O.2026-03-02

📄 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

← Previous Next →