Military Medicine

Physician Readiness for AI in Primary Care: A Cross-Sectional Survey on the Knowledge-Attitude Gap and Implementation Priorities in Switzerland

Primary care artificial intelligence adoption among United States (US) physicians accelerated from 38% to 66% within one year. Implementation strategies typically assume physician resistance as the primary barrier; however, emerging evidence suggests a different challenge where enthusiastic adoption precedes adequate knowledge development. Aims: To assess physician readiness for AI implementation ...

Beyond Accuracy: An Efficiency- and Safety-Aware Framework for Evaluating Clinical AI with Large Language Models

Large language models (LLMs) demonstrate strong performance on medical reasoning tasks, but current evaluation approaches focus primarily on accuracy, neglecting the efficiency–safety trade-offs critical for real-world clinical utility. We developed and validated the Clinical Value Density (CVD) framework, a novel metric quantifying clinical utility per unit of cognitive resource consumed. Six sta...

A Fast, Lightweight, and Generalizable Deep Neural Network for the Detection of Atrial Fibrillation

Atrial fibrillation (AFib) represents a critical diagnostic challenge in clinical cardiology, calling for automated detection systems capable of robus...

Leveraging simulation to provide a practical framework for assessing the novel scope of risk of LLMs in healthcare

Large language models (LLMs) are rapidly entering clinical care, yet their definitionally probabilistic outputs have delivered a variety of grossly un...

Breaking the Cost Barrier: How Quantization Enables Efficient Development and Deployment of LLMs for Public Healthcare

The clinical promise of Large Language Models (LLMs) is often unrealized due to pro-hibitive computational costs. These costs create barriers not only...

Physician gestalt compared with AI model to predict intubation in critically ill patients

Intubation and mechanical ventilation are associated with high mortality. Accurately predicting which patients are at the highest risk of intubation c...

OpenSlideFM: A Computationally Efficient Multi-Scale Foundation Model for Computational Pathology

Computational pathology increasingly relies on foundation models pre-trained on large-scale histopathology datasets, but existing models require subst...

Rice disease detection method based on multi-scale dynamic feature fusion.

In order to enhance the accuracy of rice leaf disease detection in complex farmland environments, and facilitate the deployment of the deep learning m...

Telemedicine in China: Effective indicators of telemedicine platforms for promoting health and well-being among healthcare consumers.

OBJECTIVE: Telemedicine platforms played a crucial role during the COVID-19 pandemic, alleviating issues related to the shortage and unequal distribut...

Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.

CONTEXT AND BACKGROUND: Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental diso...

Hybrid deep learning model for accurate and efficient android malware detection using DBN-GRU.

The rapid growth of Android applications has led to an increase in security threats, while traditional detection methods struggle to combat advanced m...

Digital transformation in healthcare management: from Artificial Intelligence to blockchain.

The digital transformation of healthcare is revolutionizing the management of medical institutions, improving operational efficiency, patient outcomes...

xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of Explainability

Reinforcement learning (RL) has shown great promise in simulated environments, such as games, where failures have minimal consequences. However, the...

[A study on post-traumatic stress disorder classification based on multi-atlas multi-kernel graph convolutional network].

Post-traumatic stress disorder (PTSD) presents with complex and diverse clinical manifestations, making accurate and objective diagnosis challenging w...

LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment

As Large Language Models (LLMs) demonstrate exceptional performance across various domains, deploying LLMs on edge devices has emerged as a new tren...

A Machine Learning Approach for Emergency Detection in Medical Scenarios Using Large Language Models

The rapid identification of medical emergencies through digital communication channels remains a critical challenge in modern healthcare delivery, p...

Artificial Intelligence in Mental Health and Well-Being: Evolution, Current Applications, Future Challenges, and Emerging Evidence

Artificial Intelligence (AI) is a broad field that is upturning mental health care in many ways, from addressing anxiety, depression, and stress to ...

MAGIC: Mastering Physical Adversarial Generation in Context through Collaborative LLM Agents

Physical adversarial attacks in driving scenarios can expose critical vulnerabilities in visual perception models. However, developing such attacks ...

Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care

The integration of artificial intelligence [AI] into clinical trials has revolutionized the process of drug development and personalized medicine. A...

Quantum Threat in Healthcare IoT: Challenges and Mitigation Strategies

The Internet of Things (IoT) has transformed healthcare, facilitating remote patient monitoring, enhanced medication adherence, and chronic disease ...