Latest AI and machine learning research in military medicine for healthcare professionals.
Benchmarks increasingly guide deployment, procurement and scientific screening, yet a score supports only the response it records, not necessarily the deployment action. We introduce deployment-complete benchmarking, which tests whether benchmark evidence determines a deployment action. A benchmark is complete for a claim exactly when the action is constant on each evidence fiber; mixed fibers exp...
Long-horizon clinical simulation -- predicting how a patient's physiology evolves over years under specified interventions -- is central to chronic-disease care, yet existing electronic health record (EHR) models are predominantly discriminative, and general-purpose large language models drift under repeated interventions. We propose the \textbf{ChronoMedicalWorld Model (CMWM)}, an action-conditio...
Despite strong predictive results in the clinical machine learning literature, the translation of these models into bedside use remains limited by sys...
Objectives: AI-based reconstructions can reduce MRI acquisition times and/or improve image quality. Guidelines recommend clinical evaluations and post...
Background: Structuring oncology clinical notes into registry-grade variables is essential for research and care but remains labour-intensive and erro...
Background Patients worldwide receive healthcare in many languages, yet medical AI systems are validated almost exclusively in high-resource languages...
Delayed diagnosis and poor antiretroviral therapy (ART) adherence remain primary drivers of HIV-related morbidity in low-resource settings, yet real-w...
Selective deployment of multiple transcription start sites is a major regulatory feature of human transcriptomes. FANTOM CAGE data exhibit a near-univ...
Post-traumatic stress disorder (PTSD) remains a significant psychiatric burden; despite growing biomarker research, no blood-based molecular diagnosti...
Generative AI systems achieve impressive performance on standard benchmarks yet fail to deliver real-world utility, a disconnect we identify across 28...
Background: Machine learning models for intensive care unit (ICU) mortality prediction achieve strong internal discrimination yet rarely undergo exter...
Pre-visit planning has the potential to reduce EHR documentation burden while improving workflow efficiency, care quality, and patient-provider engage...
Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectatio...
Large language models (LLMs) show promise in radiology but their deployment is limited by computational requirements that preclude use in resource-con...
We reframe clinician overrides of clinical AI recommendations as implicit preference data - the same signal structure exploited by reinforcement learn...
SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additiona...
Image enhancement models for mobile devices often struggle to balance high output quality with the fast processing speeds required by mobile hardware....
Efficient single-image super-resolution (SISR) requires balancing reconstruction fidelity, model compactness, and robustness under low-bit deployment,...
Fall detection in elderly care requires not only accurate classification but also reliable explanations that clinicians can trust. However, existing p...
BackgroundPlasma biomarkers demonstrate strong within-cohort performance for identifying cerebral amyloid pathology, but their real-world clinical uti...