Military Medicine

Predictive Modeling of Childhood Disease Severity at a Telemedicine Service: Evidence from the Improving Nighttime Access to Care and Treatment Studies in Haiti.

OBJECTIVE: To develop models within pediatric telemedicine that identify potentially "sick" cases for additional safety checks and integrate those models into electronic clinical decision support tools. STUDY DESIGN: We conducted a secondary analysis of paired virtual and in-person examinations across 3 consecutive implementation studies conducted at a telemedicine and medication delivery service ...

Towards lightweight stress monitoring on biometric data for IoMT environments.

BACKGROUND AND OBJECTIVE: Stress is a physiological response mechanism that enables humans to react to perceived threats through a fight-or-flight response. While beneficial in acute situations, prolonged exposure to stress can lead to significant physical and mental health issues, making early and reliable detection essential. Although many existing approaches achieve high accuracy by relying on ...

Multimodal SERS Biosensing Platforms: Emerging Opportunities for Ultrasensitive Biomarker Detection and Intelligent Diagnostics.

Surface-enhanced Raman scattering (SERS) has emerged as a powerful analytical technique for biosensing owing to its ultrahigh sensitivity and molecula...

Sepsis Diagnostics via Biosensors: Engineering Platforms, Artificial Intelligence Integration, and Clinical Translation.

Sepsis remains one of the most diagnostically challenging syndromes due to its clinical heterogeneity, overlapping host-pathogen responses, and lack o...

Small-bowel capsule endoscopy - from clinic to couch: time to go green?

PURPOSE OF REVIEW: Small-bowel capsule endoscopy (SBCE) has transformed small-intestine diagnostics by enabling direct, noninvasive mucosal visualizat...

Artificial intelligence for surgical care in war-torn sudan: Feasibility, barriers, and ethical perspectives from a conflict zone.

BACKGROUND: Artificial intelligence (AI) offers transformative potential for clinical care, yet its deployment in conflict-affected, low-resource sett...

Toward an explainable AI-Based clinical decision support system for predicting adverse outcomes in Rhabdomyolysis.

Rhabdomyolysis is a severe condition with high morbidity and mortality, driven by complications like acute kidney injury. Early risk stratification re...

How threshold customisation affects the performance of a multiclass X-ray AI model for primary care triage: a retrospective study.

OBJECTIVES: To describe the structured process of threshold optimisation for a commercially available multiclass chest X-ray (CXR) deep learning model...

From Silos to Synergy: Mapping the Adoption and Interoperability Gaps of Clinical AI in Canadian Healthcare.

Artificial intelligence (AI) is increasingly integrated into clinical workflows worldwide, yet in Canada its adoption remains fragmented, unevenly dis...

Empowering Alzheimer's and Related Dementia Prevention Through Primary Care Using Responsible AI: A Pilot Walk-in Memory Sync Booth.

Alzheimer's disease and related dementias (ADRD) remain underdiagnosed early due to reliance on costly, invasive, and time-intensive assessments, prom...

From Telemedicine to Early Warning: Embedding Artificial Intelligence in Remote Follow-Up of Diabetic Foot Ulcers.

Clinical Applications of Data Science and Machine Learning in the Pediatric Cardiac Intensive Care Unit.

To synthesize and critically appraise applications of machine learning (ML) in pediatric cardiac intensive care, focusing on algorithm performance, va...

Mechanistic signatures of comorbid PTSD with cognitive impairment implicate cortisol-induced neural toxicity.

The men and women who worked in rescue and recovery operations at the 9/11 World Trade Center site are developing cognitive impairment (CI) at mid-lif...

Data integration and systems interoperability: the prerequisite for artificial intelligence in anesthesiology.

PURPOSE OF REVIEW: Anesthesiology generates large volumes of heterogeneous perioperative data, including high-resolution physiological signals, clinic...

Early Prediction of Delirium in Postcardiac Surgery Patients: Machine Learning Model Development and External Validation.

BACKGROUND: Delirium is a frequent postoperative complication among patients who have undergone cardiac surgery and is associated with prolonged hospi...

An Adaptive Blockchain Framework for Federated IoMT with Reinforcement Learning-Based Consensus and Resource Forecasting.

The rapid growth of the Internet of Medical Things (IoMT) has increased the adoption of remote healthcare applications and telemedicine services. A Ma...

Artificial intelligence and diagnosis and management of tuberculosis disease in children.

PURPOSE OF REVIEW: The literature review is pertinent because diagnosing pediatric tuberculosis (PdTB) remains quite challenging, especially in areas ...

Machine Learning for De Novo Molecular Generation: A Comprehensive Review.

Deep generative models have emerged as powerful computational engines for de novo molecular design, enabling efficient exploration of a vast chemical ...

Digitized Canine Olfaction and Multimodal Biosensing for Breath-Based Multi-Cancer Detection: A Hypothesis-Driven Perspective.

Multi-Cancer Early Detection (MCED) is critical for reducing cancer mortality, however current screening technologies have limitations in accessibilit...

Mechanistic interpretability of reinforcement learning in Medicaid care coordination.

OBJECTIVE: To expose reasoning pathways of a reinforcement learning policy for Medicaid care coordination, develop an error taxonomy and implement fai...