Military Medicine

Artificial intelligence assisted telemedicine, clinical decision support for anesthesia and critical care in intensive care units: a scoping review.

BACKGROUND: Artificial intelligence (AI) has been increasingly used in care delivery in intensive care units (ICUs) and anesthesia-critical care practice through telemedicine, tele-ICU systems, and remote patient monitoring, and is expected to support real-time clinical decision-making. METHODS: This scoping review followed PRISMA-ScR guidelines to map the existing evidence of AI in critical care ...

Artificial intelligence for carbon emissions management: advances, challenges, and future directions across monitoring, prediction, and reduction.

Rising anthropogenic carbon emissions are a major driver of climate change and pose a critical challenge to global sustainable development. As a rapidly advancing technology, artificial intelligence (AI) has shown strong potential to enhance carbon emissions management. This review provides a critical and comprehensive synthesis of recent advances in AI-enabled approaches for carbon emissions moni...

Attitudes and Needs of Health Care Providers Toward Artificial Intelligence-Assisted Pediatric Palliative Care: Mixed Methods Study.

BACKGROUND: While artificial intelligence's (AI's) transformative potential in health care is widely acknowledged, its application in highly sensitive...

Machine learning for risk stratification of hypertensive disorders of pregnancy: Enhancing clinical efficiency in low-resource antenatal care in Tanzania.

Maternal mortality in Tanzania remains a public health crisis, with Hypertensive Disorders of Pregnancy (HDP) causing 34% of direct obstetric deaths. ...

Expatriate health care workers in Saudi Arabia: Exploring workforce integration, advantages, and challenges in a globalized health care system.

BACKGROUND: Effective expatriate management has become crucial in the health care sector, driven by the growing number of globally mobile professional...

Development and validation of a dual-channel deep learning for continuous acute kidney injury prediction in critically ill patients.

This study aimed to develop and externally validate a real-time, continuous prediction model for 48-h acute kidney injury (AKI) risk in critically ill...

Real-time dynamic monitoring of rockfalls with PTZ cameras.

Rockfall is a prevalent geological hazard threatening lives and infrastructure. Beyond static assessment, real-time dynamic monitoring is crucial to c...

TRUST: an large language model-based dialogue system for trauma understanding and structured assessments.

OBJECTIVES: While large language models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue...

Advances in Remote Monitoring Technology Applications in Anesthesia: A Narrative Review.

The swift progress of digital and sensor technologies is hastening the incorporation of remote monitoring into anesthesiology. While several reviews h...

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

Accurate skin lesion segmentation is essential for the early detection and effective management of skin cancer. Existing deep learning architectures a...

AI in clinical diagnostics in dermatology: applications, validation, and real-world use cases.

Artificial intelligence (AI) has moved from proof-of-concept studies in dermatology to selective, real-world clinical use, particularly in image-based...

Predictive analytics and risk stratification models in internal medicine: from risk scores to real-time machine learning.

Internal medicine involves high-stakes, time-sensitive decisions (such as triaging acute illnesses, escalating care, providing thromboprophylaxis, pla...

Impact of feature engineering on predicting non-contact football injuries.

Non-contact injuries in professional football impose significant performance and economic burdens, yet the influence of workload feature representatio...

Survivor Perspectives on Artificial Intelligence Integration in Breast Cancer Treatment: A Qualitative Study of Trust, Equity, and Application.

FUNDING: Funding (NIH #1OT2OD037972-01). OBJECTIVE: Artificial intelligence (AI) may enhance the efficiency and personalization of breast cancer (BC) ...

Clinical artificial intelligence (AI) in Japan: Regulatory pathways, domain-specific evidence, and its data infrastructure from an international perspective.

Artificial intelligence (AI) has advanced rapidly across clinical domains, generating both a growing evidence base and dedicated regulatory frameworks...

From Data to Decision: Standardization, Validation, and Regulatory Readiness of AI-Driven Analytical Chemistry Workflows.

Artificial intelligence (AI) is fundamentally reshaping analytical chemistry by enabling automated data interpretation, intelligent method optimizatio...

Reference Standards, Numerical Consistency, and Scoring Logic: Pre-deployment Assurance Considerations for AI-Assisted Third Molar Extraction Planning : Regarding: Ko J, Sooksatra S, Kim S, Tang S, Ha JE, Han DH, Lee KH, Jung YJ, Kim MJ. Quantitative assessment of third molar extraction difficulty and nerve injury risk using artificial intelligence and image processing. Ann Biomed Eng. 2026. https://doi.org/10.1007/s10439-026-04114-9.

A Futures Framework for Clinical AI Governance: Anticipating Emerging Risks, Shifting Roles, and Regulatory Challenges.

This viewpoint develops the futures framework for clinical artificial intelligence governance (FF-CAIG), a conceptual and anticipatory framework for o...

A Clinically Interpretable AI System for Real-Time Quality Control of Transthoracic Echocardiography: Development, Validation, and Deployment.

BACKGROUND: Quality control (QC) in echocardiography is crucial but is often subjective, retrospective, and labor-intensive. Artificial intelligence (...

A cloud-edge adaptive lightweight network with dynamic inference enables real-time ultrasound image segmentation.

Ultrasound image segmentation plays an important role in clinical diagnosis, but existing deep learning methods struggle to balance accuracy and effic...