Military Medicine

CALLM: Enhancing Clinical Interview Analysis Through Data Augmentation With Large Language Models.

The global prevalence of mental health disorders is increasing, leading to a significant economic burden estimated in trillions of dollars. In automated mental health diagnosis, the scarcity and imbalance of clinical data pose considerable challenges for researchers, limiting the effectiveness of machine learning algorithms. To cope with this issue, this paper aims to introduce a novel clinical tr...

Predicting mental health treatment outcomes using latent growth mixture models and machine learning in a real-world clinical setting.

Previous models of depression outcomes have been limited by symptom heterogeneity within populations. This study conducted a retrospective analysis using latent growth mixture models to identify heterogeneous trajectories within a clinical population, subsequently developing machine learning models to predict clinical outcomes based on baseline characteristics and symptom measures. The study analy...

A randomized controlled trial on evaluating clinician-supervised generative AI for decision support.

BACKGROUND: The integration of generative artificial intelligence (AI) as clinical decision support systems (CDSS) into telemedicine presents a signif...

The role of artificial intelligence in enhancing healthcare for people with disabilities.

The integration of artificial intelligence (AI) in healthcare delivery represents a transformative opportunity to enhance the lives of people living w...

Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring.

Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-c...

Improving care for amyotrophic lateral sclerosis with artificial intelligence and affective computing.

BACKGROUND: Patients with ALS often face difficulties expressing emotions due to impairments in facial expression, speech, body language, and cognitiv...

Assessing the Usability and Effectiveness of an AI-Powered Telehealth Platform: Mixed Methods Study on the Perspectives of Patients and Providers.

BACKGROUND: Telemedicine has revolutionized health care by significantly enhancing accessibility and convenience, yet barriers remain, such as provide...

Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment.

Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes an...

AI solutions for overcoming delays in telesurgery and telementoring to enhance surgical practice and education.

Artificial intelligence (AI) has emerged as a transformative tool in surgery, particularly in telesurgery and telementoring. However, its potential to...

Testing process for artificial intelligence applications in radiology practice.

Artificial intelligence (AI) applications are becoming increasingly common in radiology. However, ensuring reliable operation and expected clinical be...

Unreal that feels real: artificial intelligence-enhanced augmented reality for treating social and occupational dysfunction in post-traumatic stress disorder and anxiety disorders.

Fear- and trauma-related conditions, such as post-traumatic stress disorder (PTSD) and social phobia, often manifest as socially avoidant behaviours,...

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. Howe...

Deployment of Artificial Intelligence in Radiology: Strategies for Success.

Radiology, as a highly technical and information-rich medical specialty, is well suited for artificial intelligence (AI) product development, and many...

Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.

Acute kidney injury (AKI) is a significant health challenge associated with adverse patient outcomes and substantial economic burdens. Many authors ha...

Intelligent wearable-assisted digital healthcare industry 5.0.

The latest evolution of the healthcare industry from Industry 1.0 to 5.0, incorporating smart wearable devices and digital technologies, has revolutio...

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist.

Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF...

A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization.

Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the mater...

Is artificial intelligence prepared for the 24-h shifts in the ICU?

Integrating machine learning (ML) into intensive care units (ICUs) can significantly enhance patient care and operational efficiency. ML algorithms ca...

Using machine learning modeling to identify childhood abuse victims on the basis of personality inventory responses.

Trauma is very common and associated with significant co-morbidity world-wide, particularly PTSD and frequently other mental health disorders. However...

Global Suicide Mortality Rates (2000-2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data.

Suicide research is directed at understanding social, economic, and biological causes of suicide thoughts and behaviors. (1) Background: Worldwide, ce...