Latest AI and machine learning research in military medicine for healthcare professionals.
Accurate and interpretable detection of depressive language in social media is useful for early interventions of mental health conditions, and has important implications for both clinical practice and broader public health efforts. In this paper, we investigate the performance of large language models (LLMs) and traditional machine learning classifiers across three classification tasks involving...
This position paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient testing frameworks as a principled foundation for ensuring reliability and safety in real-world deployment. A recent review found that only 9% of FDA-registered AI-based healthcare tools include a post-deployment surveillance plan. Existing monitoring a...
The distribution of data changes over time; models operating operating in dynamic environments need retraining. But knowing when to retrain, without...
Complete blood cell detection holds significant value in clinical diagnostics. Conventional manual microscopy methods suffer from time inefficiency ...
Skin cancer is among the most prevalent and life-threatening diseases worldwide, with early detection being critical to patient outcomes. This work ...
Integrating Traditional Chinese Medicine (TCM) and Modern Medicine faces significant barriers, including the absence of unified frameworks and standar...
Similar to major natural disasters and large-scale wars, the events of October 7th and the retaliatory Iron Swords war resulted in both direct and ind...
In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is al...
Although still limited, the integration of artificial intelligence (AI) in health care has rapidly expanded in the past few years, especially in oncol...
Deep learning (DL) has the potential to deliver significant clinical benefits. In recent years, an increasing number of DL-based systems have been app...
Retinal imaging has emerged as a powerful, non-invasive modality for detecting and quantifying biomarkers of systemic diseases-ranging from diabetes...
Foundation models like CLIP (Contrastive Language-Image Pretraining) have revolutionized vision-language tasks by enabling zero-shot and few-shot le...
Transformer-based models have shown strong performance across diverse time-series tasks, but their deployment on resource-constrained devices remain...
Transformer-based models have shown strong performance across diverse time-series tasks, but their deployment on resource-constrained devices remain...
Can small language models with 0.5B to 5B parameters meaningfully engage in trauma-informed, empathetic dialogue for individuals with PTSD? We addre...
BACKGROUND: Telemedicine, which incorporates artificial intelligence such as chatbots, offers significant potential for enhancing health care delivery...
Over the years, ComBAT has become the standard method for harmonizing MRI-derived measurements, with its ability to compensate for site-related addi...
Suicide remains one of the main preventable causes of death among active service members and veterans. Early detection and prediction are crucial in...
Telecardiology has emerged as a promising approach in acute cardiac care through advancements in digital health technologies. This review explores the...
This study analyzes 159 master's theses in Medical Informatics from the University of Porto, spanning 2006 to 2023, to identify key trends, thematic f...