Latest AI and machine learning research in military medicine for healthcare professionals.
Ambient artificial intelligence (AI) offers the potential to reduce documentation burden and improve efficiency through clinical note generation. Widespread adoption, however, remains limited due to challenges in electronic health record (EHR) integration, coding compliance, and real-world evaluation. This study introduces a framework and protocols to design, monitor, and deploy ambient AI within ...
Sepsis remains a leading cause of mortality in intensive care units (ICUs) worldwide, underscoring the urgent need for early detection to improve patient outcomes. While artificial intelligence (AI) models trained on ICU data show promise for sepsis prediction, their clinical utility is frequently hampered by poor generalization under external validation, largely attributable to distribution shift...
Unplanned hospital admissions impose substantial strain on healthcare systems, yet predictive models for these events remain underexplored in practice...
GAI tools are increasingly used informally for health, yet evidence from low- and middle-income countries (LMICs) is limited. This study generates ear...
Persistent socioeconomic and caste inequalities in India drive disparities in healthcare access. Machine learning (ML) models offer promise for foreca...
Cancer cachexia, a multifactorial metabolic syndrome characterized by severe muscle wasting and weight loss, contributes to poor outcomes across vario...
Standardized assessment of clinical quality measures from electronic health records (EHRs) is challenging because information is fragmented across str...
Maternal and child health (MCH) represents a critical domain requiring accurate, timely, and data-driven decision-making to optimize outcomes from pre...
Hospital readmissions represent a persistent challenge for healthcare systems, often stemming from inadequate post-discharge monitoring. This study pr...
Artificial intelligence (AI) applied to routine electrocardiograms (ECGs) offers promise for screening of structural heart disease (SHD), yet broad cl...
Primary care artificial intelligence adoption among United States (US) physicians accelerated from 38% to 66% within one year. Implementation strategi...
Large language models (LLMs) demonstrate strong performance on medical reasoning tasks, but current evaluation approaches focus primarily on accuracy,...
Atrial fibrillation (AFib) represents a critical diagnostic challenge in clinical cardiology, calling for automated detection systems capable of robus...
Large language models (LLMs) are rapidly entering clinical care, yet their definitionally probabilistic outputs have delivered a variety of grossly un...
Accurate, affordable tuberculosis (TB) diagnostics that do not require sputum samples are urgently needed for TB control and elimination. Prior serolo...
The clinical promise of Large Language Models (LLMs) is often unrealized due to pro-hibitive computational costs. These costs create barriers not only...
Intubation and mechanical ventilation are associated with high mortality. Accurately predicting which patients are at the highest risk of intubation c...
Computational pathology increasingly relies on foundation models pre-trained on large-scale histopathology datasets, but existing models require subst...
In order to enhance the accuracy of rice leaf disease detection in complex farmland environments, and facilitate the deployment of the deep learning m...
OBJECTIVE: Telemedicine platforms played a crucial role during the COVID-19 pandemic, alleviating issues related to the shortage and unequal distribut...