health informatics papers

An End-to-End Synthetic Oncology Clinical Trial Framework Integrating Radiographic Response, Circulating Tumor DNA, Safety, and Survival for Decision-Oriented Clinical Data Science

This study presents a comprehensive, literature-informed synthetic phase II oncology clinical trial framework that successfully integrates radiographic, molecular (ctDNA), safety, and survival data to generate a biologically plausible, analytically coherent efficacy-safety signal, thereby serving as a decision-oriented prototype for translational clinical data science.

Petalcorin, M. I. R.2026-04-08📄 health informatics

Using Relative Risk Rankings to Understand Information Differences in Multimodal Prediction Models

This study demonstrates that replacing raw chest radiographs with expert-written reports in multimodal mortality prediction models leads to significant information loss and altered risk prioritization, suggesting that text summaries are imperfect proxies for visual prognostic cues.

Kim, C., Yoon, W., Lee, H. + 4 more2026-04-07📄 health informatics

Who is leading medical AI? A systematic review and scientometric analysis of chest x-ray research

This systematic review and scientometric analysis of 928 chest X-ray AI studies reveals that research leadership and training data are overwhelmingly dominated by high-income countries, particularly the US and China, creating significant disparities that risk developing AI systems with inconsistent performance across diverse global populations and exacerbating healthcare inequities.

Vasquez-Venegas, C., Chewcharat, A., Kimera, R. + 18 more2026-04-07📄 health informatics

Perception of Safety in Behavioral Health Crisis Units among Patients and Care Partners versus Artificial Intelligence (AI): A Multimethod Study

This multimethod study reveals that while perceived safety significantly influences patient and care partner facility selection in behavioral health crisis units, there are notable discrepancies between human and AI-identified environmental risks, underscoring the value of integrating AI tools to support safer decision-making while acknowledging their current limitations in capturing nuanced human perceptions.

Jafarifiroozabadi, R.2026-04-07📄 health informatics

Attitudes and Perceptions Toward the Use of Artificial Intelligence Chatbots for Peer Review in Medical Journals: A Large-Scale, International Cross-Sectional Survey

This large-scale international survey reveals that while medical journal peer reviewers are highly familiar with AI chatbots, their actual use in peer review remains limited due to significant ethical concerns and a lack of institutional training, despite a strong expressed interest in receiving guidance for future implementation.

Ng, J. Y., Bhavsar, D., Dhanvanthry, N. + 9 more2026-04-07📄 health informatics

High-Throughput Observational Evidence Generation Using Linked Electronic Health Record and Claims Data

This paper presents a high-throughput workflow utilizing linked electronic health record and claims data to generate standardized, comprehensive evidence packages across 40 clinical domains, thereby shifting comparative effectiveness research from fragmented studies toward a cohesive evidence base that supports precision medicine and reduces redundant stakeholder-specific investigations.

Gombar, S., Shah, N., Sanghavi, N. + 3 more2026-04-07📄 health informatics

Perioperative Mortality Prediction Using a Bayesian Ensemble with Prevalence-Adaptive Gating

This study presents a prevalence-adaptive Bayesian ensemble model that utilizes Variational Autoencoder-based data augmentation and entropy-driven uncertainty quantification to achieve perfect separation on a validation cohort and clinically meaningful sensitivity with zero false positives in predicting perioperative mortality within resource-limited surgical settings.

Pandey, A. K.2026-04-06📄 health informatics

TELF: An End-to-End Temporal Encoder with Late Fusion for Interpretable Disease Risk Prediction from Longitudinal Real-World Data

The paper introduces TELF, a lightweight, end-to-end temporal encoder with late fusion that bypasses resource-intensive pre-training to achieve superior predictive performance and interpretable clinical trajectory mining for disease risk prediction using longitudinal real-world data.

Liu, Y., Zhang, Z.2026-04-06📄 health informatics

Sound of Aging: Large-Scale Evidence for a Voice-Based Biological Clock

This study demonstrates that voice-based models trained on 7,081 adults can accurately predict biological age and identify accelerated aging linked to cardiometabolic risks, establishing voice as a scalable, non-invasive biomarker for health monitoring.

Krongauz, D., Marmor, Y., Zulti, A. + 3 more2026-04-06📄 health informatics

Digital Registrar: A Schema-First Framework for Multi-Cancer Privacy-Preserving Pathology Abstraction via Local LLMs

This paper presents "Digital Registrar," a schema-first framework that leverages local large language models and a CAP-aligned clinical ontology to accurately transform free-text surgical pathology reports into structured, privacy-preserving registry data across multiple cancer types, achieving high accuracy and generalizability while decoupling clinical logic from specific AI models.

Chow, N.-H., Chang, H., Chen, H.-K. + 7 more2026-04-05📄 health informatics

CD276 in Meningioma Transcriptomic Classification: Internal Development, External Validation, and Stability-Informed Interpretation

This study demonstrates that while CD276 is significantly associated with meningioma grade, it functions as a biologically relevant target-of-interest rather than a dominant standalone predictor, with the model's robust performance and stability relying on a broader multigene transcriptomic structure that requires conservative, calibration-aware interpretation.

Lee, H., Kim, H.2026-04-05📄 health informatics

Reproducibility and Robustness of Large Language Models for Mobility Functional Status Extraction

This study evaluates the reproducibility and robustness of three distinct large language models in extracting mobility functional status from clinical narratives, demonstrating that while prompt variations and higher temperatures can significantly degrade stability, self-consistency via majority voting offers an effective mitigation strategy to enhance reliability without sacrificing predictive performance.

Liu, X., Garg, M., Jeon, E. + 4 more2026-04-05📄 health informatics

Electronic Health Record-Based Estimation of Kansas City Cardiomyopathy Questionnaire Scores in Heart Failure

This study demonstrates that machine learning models trained on electronic health record data can accurately estimate Kansas City Cardiomyopathy Questionnaire scores and classify heart failure severity, offering a viable solution to overcome the limitations of incomplete patient-reported outcome data collection.

Kim, Y. W., Lau, W., Patel, N. + 5 more2026-04-05📄 health informatics

Extracting Social Determinants of Health from Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods

This study demonstrates that large language models, particularly advanced "mini" variants, outperform rule-based systems in extracting social determinants of health from unstructured clinical notes, with the highest accuracy achieved by a late-fusion ensemble combining both approaches.

Wang, B., Kabir, D., Clark, C. R. + 2 more2026-04-04📄 health informatics

Enhancing Medical Knowledge in Large Language Models via Supervised Continued Pretraining on Clinical Notes

This study demonstrates that supervised continued pre-training of a 4B-parameter LLM on 500,000 de-identified clinical notes significantly enhances its performance on real-world medical decision-making tasks and specific clinical benchmarks, surpassing larger untrained models while successfully retaining general-domain knowledge.

Weissenbacher, D., Shabbir, M., Campbell, I. M. + 2 more2026-04-04📄 health informatics

Med-ICE: Enhancing Factual Accuracy in Medical AI through Autonomous Multi-Agent Consensus

Med-ICE is an autonomous multi-agent framework that enhances the factual accuracy and reliability of medical AI by employing an iterative peer-review consensus mechanism to eliminate hallucinations and outperform existing single-model and self-refinement approaches.

Chen, Z., Wu, R., Liu, Y. + 2 more2026-04-04📄 health informatics

Citation Hallucination Determines Success: An Empirical Comparison of Six Medical AI Research Systems

This paper introduces MedResearchBench to evaluate six medical AI research systems, revealing that citation hallucination is the decisive factor in their success and demonstrating that multi-agent verification pipelines significantly outperform subjective single-model evaluations in ensuring trustworthy scientific writing.

Shi, X., Tian, Z., Tan, S. + 1 more2026-04-04📄 health informatics

Multi-Task Learning and Soft-Label Supervision for Psychosocial Burden Profiling in Cancer Peer-Support Text

This study demonstrates that composite-only multi-task learning effectively profiles multidimensional psychosocial burdens in cancer peer-support text, while soft-label supervision derived from large language models underperforms compared to hard-label baselines for emotion classification.

Wang, Z., Cao, Y., Shen, X. + 3 more2026-04-04📄 health informatics

Automated detection of adult autism from vowel acoustics using machine learning

This study demonstrates that machine learning models trained on acoustic features of vowel production, particularly fundamental frequency, can effectively distinguish autistic adults from neurotypical individuals with up to 89% accuracy, suggesting a promising, scalable, and interpretable tool for autism spectrum disorder screening.

Georgiou, G. P., Paphiti, M.2026-04-04📄 health informatics

Corpus for Benchmarking Clinical Speech De-identification

To address the scarcity of publicly available resources for clinical speech de-identification, this paper introduces the SREDH-AICup corpus, a time-aligned dataset comprising 20 hours of English and Mandarin audio annotated with 7,830 sensitive health information entities across 38 categories to support automated privacy protection research.

Dai, H.-J., Fang, L.-C., Mir, T. H. + 11 more2026-04-03📄 health informatics