Military Medicine

[Use of artificial intelligence for next-gen anamnesis and communication in orthopedics & trauma surgery : From chatbots to ambient intelligence].

BACKGROUND: Against the backdrop of increasing patient volumes, rising case complexity, and physicians' limited time, AI-driven systems for anamnesis, triage, and documentation offer substantial potential for efficiency gains in medical history taking and clinical communication. Their development spans from rule-based decision trees to machine learning and large language model (LLM) dialogues, and...

Interpretable Semantic Medical Image Segmentation with Style and Confidence.

The scarcity of semantically labelled data presents major challenges for medical image segmentation using deep learning models, and the "black-box" nature of these models inherently limits their interpretability during clinical deployment. To address these issues, we propose Generative Adaptable Segmentation Evolution (GASE), an end-to-end, style-based generative adversarial framework for robust a...

Deep learning models for acute kidney injury prediction: multi-center external validation and evaluation under simulated continuous monitoring conditions.

Acute kidney injury (AKI) is a common hospital complication with substantial morbidity and mortality. Deep learning models for AKI prediction show str...

Agentic GPT-5.0 system outperforms standard large language models and human experts in critical care clinical decision-making: a simulation study.

BACKGROUND: Agentic artificial intelligence (AI) systems employing multi-model architectures with iterative reasoning may surpass standard single-mode...

Artificial intelligence algorithm for real-time detection and counting of Trypanosoma cruzi parasites using smartphone microscopy.

Chagas disease affects 6-7 million people worldwide and causes approximately 12,000 deaths annually. Diagnostic methods vary by disease stage, with se...

From Pilot Trap to Institutional Capacity: A Governance Framework for Sustainable Clinical AI Implementation in Health Systems.

Clinical artificial intelligence (AI) applications frequently fail to transition from short-term pilot projects into sustained components of routine c...

Smartphone-based lightweight AI system for real-time multiple anterior segment disease screening: development and real-world validation.

BACKGROUND: Anterior segment diseases are a major global cause of preventable blindness, especially in regions with limited access to specialized opht...

Examining the utility of digital phenotyping for the prediction of intrusive experiences.

Background: Intrusive experiences related to witnessing a traumatic event are the core symptom of post-traumatic stress disorder (PTSD), and have been...

Accurate estimation of hip range of motion using MediaPipe and inertial sensors with machine learning models.

Accurate, objective assessment of hip joint range of motion (ROM) is essential for orthopedic diagnosis and rehabilitation. Conventional tools, such a...

From mechanisms to machine learning: an AI-enabled framework for biochar-based remediation of polyaromatic hydrocarbons (PAHs).

Biochar is a carbon-rich byproduct of biomass pyrolysis. It has emerged as a promising tool in environmental biomanufacturing specifically for the rem...

Executive summary of the 2026 consensus document. Planning the future of Internal Medicine: A position statement of the International Forum of Internal Medicine (FIMI).

The International Forum of Internal Medicine (FIMI) presents a position paper that analyzes the current state and projects the future of Internal Medi...

When AI Colludes: Clinical Reliability of Training and Preference Data as a Trustworthy-AI Criterion.

BACKGROUND: Research on artificial intelligence (AI) and mental health has focused largely on harms at deployment, including chatbot safety, sycophanc...

Adaptive homomorphic federated learning framework for multi-institutional medical imaging with optimized diagnostic accuracy.

The growing use of artificial intelligence (AI) in medicine has highlighted the imperative for privacy-preserving and high-accuracy diagnostic systems...

Data-driven outcome prediction and rational resource allocation in severe traumatic brain injury: a multicenter retrospective study integrating survival analysis and machine learning ensembles.

INTRODUCTION: Severe traumatic brain injury (sTBI), is a leading cause of death and disability among young and middle-aged populations worldwide. OBJE...

A Bilingual AI-Based Chatbot for Nutrition Education in a Food Is Medicine Intervention for High-Risk Pregnant Women: Design and Development Study.

BACKGROUND: Conversational agents (artificial intelligence [AI]-based chatbots) offer a novel approach to health interventions by providing personaliz...

Structural optimization principles for edge AI in motorsport telemetry.

In motorcycle motorsport, engineering decisions are constrained by mass, stiffness, fatigue life, and thermal margins. In a comparable way, deploying ...

Crack-Expansion Patterned Laser-Induced Graphene Strain Sensors for Machine Learning-Assisted Neck Posture Monitoring.

Prolonged exposure to electronic devices exacerbates neck health issues in modern populations. To address this challenge, we develop a flexible strain...

Artificial Intelligence-Powered Simulation for Telehealth Communication Training: Bridging Traditional Simulation and Artificial Intelligence.

INTRODUCTION: Traditional simulation-based communication training remains resource-intensive and difficult to scale. While artificial intelligence (AI...

Systems of Care in Cardiogenic Shock: Team-Based Management, Shock Networks, and Post Discharge Pathways.

Cardiogenic shock (CS) remains the leading cause of mortality in modern cardiac intensive care unit, most often precipitated by acute or chronically d...

CADP: Connection-Aware DenseNet Pruning for lightweight plant disease classification.

Plant diseases threaten global agriculture, and deep learning-based disease recognition has become crucial for addressing this challenge. While DenseN...