Military Medicine

Deployment process for artificial intelligence applications in radiology practice.

Successful deployment of medical artificial intelligence (AI) systems should start with formulating clear goals and understanding organisational workflows. Comprehensive deployment planning is advised after identifying a suitable product, preliminary testing, and procurement. A structured approach supports effective adoption and improved efficiency. Planning involves evaluating the feasibility of ...

Prospective Validation of Multi-week Inpatient Census Forecasting for Pediatric HSCT and Cellular Therapy.

BACKGROUND: Accurate inpatient census forecasting is important for cell therapy and blood and marrow transplantation (BMT) programs because bed capacity, staffing, and coordination of transplant care depend on anticipating occupancy several weeks in advance. Forecasting BMT census is challenging because the service combines planned admissions, including scheduled transplants, with less predictable...

An end-to-end deep learning pipeline for urban noise monitoring with spatiotemporal analysis.

Urban noise pollution is a critical public health problem affecting millions of people around the world. Current acoustic monitoring systems largely f...

A lightweight deep learning model for real-time in-vehicle driver distraction detection with low-latency inference.

Driver distraction is a major road-safety concern that requires reliable and efficient in-vehicle monitoring systems. The main contribution of this wo...

A deep-learning based gait classification and anomaly detection framework for healthcare surveillance.

Gait classification and anomaly detection are non-intrusive approaches that can support healthcare surveillance and clinical gait analysis by identify...

Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.

Artificial intelligence (AI) has emerged as a transformative tool for improving the detection, prediction, and prevention of adverse drug reactions (A...

AI-Enhanced Predictive Analytics to Optimize Tele-Oncology Implementation in Rural Settings: Scoping Review.

BACKGROUND: Tele-oncology addresses geographic barriers to cancer care, but implementation challenges persist in rural settings. AI-enhanced predictiv...

An Acceptance Criteria Framework for Determining the Implementation Fit of Custom Large Language Models in Public Health Interventions.

Large language models (LLMs) are increasingly embedded in clinical and population health workflows, including conversational agents such as health cha...

The Rise of AI Companions: From Prediction to Surgery in the Digital Twin Era.

Artificial intelligence (AI) is rapidly reshaping orthopaedic surgery, supported by advances in data science, computational power, and perioperative d...

Palliative Care Physicians' Perceptions about Using Artificial Intelligence for Prognostication.

CONTEXT: Statistical and artificial intelligence (AI)-based methods have informed clinical prognostication for decades, evolving into machine learning...

"Check Symptoms & Get Care": Mount Sinai's AI Triage Solution.

Mount Sinai Health System (MSHS), one of New York City's largest academic medical centers, faced a common patient-access challenge: individuals presen...

From Naturally Occurring to Engineered Nano-Biochar: Properties, Feasibility, and Challenges for Sustainable Environmental Remediation.

Nano-biochar, present both as naturally occurring pyrogenic carbon and as an engineered product of controlled pyrolysis, with particle sizes typically...

Bouncing back from stress: objective markers of expressive flexibility and resilience in emergency healthcare workers using computer vision.

Healthcare workers (HCWs) in emergency departments face significant mental health risk due to chronic stressors and repeated trauma, yet symptom under...

Machine learning-enhanced 3GPP channel modeling for 5G networks: A vendor-calibrated framework with cross-scenario validation.

Accurate channel characterization across diverse propagation environments is foundational to 5G network planning, yet existing machine learning approa...

Artificial Intelligence in Neonatal Care: The Breadth of Promise, the Depth of Challenge-An Overview.

Artificial intelligence (AI) is becoming an integral tool in clinical care. The recent position statement by the Royal Australasian College of Physici...

Nonverbal AI-Based Communication Robot for Staff in Disaster-Affected Care Facilities: Exploratory ABAB Intervention Study.

BACKGROUND: Medical and welfare facilities in the Noto region of Japan were severely affected by the 2024 Noto Peninsula earthquake and subsequent tor...

A unified multi-modal foundation model for end-to-end emergency care.

Against the backdrop of population aging and the rising burden of chronic diseases in China, emergency departments (EDs) in tertiary hospitals remain ...

Uncertainty-guided mamba network for efficient medical image segmentation with evidential deep learning.

Medical image segmentation requires balancing accuracy, computational efficiency, and uncertainty quantification for potential clinical deployment. Tr...

The role of health literacy and attitudes toward artificial intelligence in the acceptance of telemedicine services among adults in Turkey: a cross-sectional study.

BACKGROUND: The digital transformation in healthcare has led to an increase in the use of telemedicine and artificial intelligence (AI)based applicati...

Digital Management of Early-Onset Type 2 Diabetes: Empowerment, Challenges, and Future Outlook.

PURPOSE OF REVIEW: Early-onset type 2 diabetes (EOT2D), defined as a diabetes diagnosis before 40 years of age, is rising globally and associated with...