Military Medicine

State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with diagnostic disparities, particularly pronounced in resource-constrained and decentralized healthcare settings. Recent advances in TinyML machine learning models optimized for ultra-low-power, memory-constrained embedded devices have created new opportunities for scalable on-device CRC screening and diagnost...

Value operating system: a practical framework to measure outcomes-per-cost at the episode level.

OBJECTIVE: To specify a value operating system (VOS) and its executable metric-the Value Index (VI)-that expresses risk-adjusted outcomes-per-episode-cost on a 0-100 scale with 95% CIs, including transparency guardrails and equity checks, and to outline two prospective exemplars for clinical deployment. DESIGN: Methods framework with prospective service evaluation and a predefined sensitivity plan...

A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).

BACKGROUND: The prevalence of post-traumatic stress disorder (PTSD) among South Korean firefighters is likely to be under-reported because of stigma a...

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment.

Purpose To investigate whether deep learning models trained on chest radiographs (CXRs) rely on radiographic exposure parameters as shortcut features ...

Artificial Intelligence and Algorithmic Bias in Burn Care: A Literature Review of Ethical Challenges.

Artificial intelligence (AI) is increasingly integrated into burn care for triage, burn-depth assessment, prognostic scoring, pain management, and tel...

[Beyond the bladder-a specialty rediscovering itself: Outpatient. Networked. Digital].

BACKGROUND: Urology is undergoing a fundamental transformation characterized by increasing outpatient care, digitalization, and cross-sectoral network...

Enhancing Physician Resilience to Generative AI: Multilevel Framework for Shared Authority, Verification, and Skill Preservation.

As generative artificial intelligence (AI), particularly large language model-based tools, is increasingly integrated into diagnosis, triage, decision...

Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review.

BACKGROUND: Driven by recent advances in artificial intelligence (AI), particularly in medicine, audio-based voice and speech biomarkers are increasin...

[Digital transformation in multiple sclerosis: Advances in diagnostics, monitoring and patient-centred care].

Digital transformation is fundamentally changing the diagnosis, monitoring and treatment of multiple sclerosis. The integration of multimodal data fro...

Preoperative considerations for nonoperating room anesthesia.

PURPOSE OF THE REVIEW: This review aims to address the unique challenges in nonoperating room anesthesia (NORA) locations, emphasizing the importance ...

EdgeVolution: democratizing multi-objective neural architecture search and end-to-end deployment on microcontrollers.

Edge AI holds great potential for extending the use of artificial neural networks to resource-constrained edge devices, such as microcontrollers. Desp...

Automated Summarization of Military Medical Records Using AI: Evaluation Via the OPTICA Framework.

INTRODUCTION: Military medical fitness evaluations require physicians to rapidly review extensive and heterogeneous medical records to determine servi...

GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management.

Plant diseases cause 20-40% annual crop losses worldwide, yet conventional detection methods remain slow, subjective, and inaccessible to smallholder ...

From black box to glass box: explainable artificial intelligence for acute kidney injury prediction-a scoping review and the GLASS-AKI translational framework proposal.

BACKGROUND: Artificial intelligence models for acute kidney injury (AKI) prediction achieve strong discriminative accuracy, yet clinical adoption rema...

From technology adoption to noise reduction: Behavioral and psychological dimensions of clinical artificial intelligence deployment.

For more than five decades, patients with the same condition have received markedly different care depending on which clinician they happen to see. Th...

Learning-based orchestration for low-latency AI deployment in hybrid cloud-edge platforms.

The rapid growth of AI-driven applications in hybrid cloud-edge environments poses substantial challenges to ensuring low latency, high throughput, an...

Beyond Validation: Operationalising Post-Deployment Surveillance of AI Medical Devices in Clinical Practice.

Artificial intelligence medical devices are increasingly deployed in clinical practice, yet practical approaches to post-deployment monitoring remain ...

Novel technologies and innovations in postoperative follow-up after regional anesthesia.

PURPOSE OF REVIEW: Postoperative follow-up after regional anesthesia is essential for identifying complications, distinguishing expected block effects...

Neuroimmune mechanisms in PTSD: inflammatory heterogeneity, circuit dysfunction, and biomarker-based subtypes.

Post-traumatic stress disorder (PTSD) is characterized by exaggerated fear response, anxiety, hyperarousal and sleep disturbances. One of the major pa...

A selective deep learning framework for pressure injury staging with calibrated confidence and automated clinical documentation.

OBJECTIVES: Pressure injuries are common chronic wounds that require accurate staging to guide management. Deep learning has shown promise for automat...