Large language model-based virtual patients (LLM-VPs) are an emerging simulation tool for health professions education, but their design and integration into curricula are not well characterized. This scoping review mapped how LLM-VPs are being used...
Despite widespread adoption, Electronic Medical Record (EMR) systems remain limited in providing intelligent clinical decision support, particularly for early detection of patient deterioration. We present MedROAD V2 (Medical Records Organization, An...
The rapid integration of artificial intelligence (AI) into mental healthcare presents opportunities and ethical challenges, particularly for complex conditions like obsessive–compulsive disorder (OCD). In this perspective, we argue for a Dual I...
Artificial intelligence (AI) is rapidly transforming healthcare through applications in clinical decision support, diagnostic imaging, population health management, and workflow optimization. Despite these advances, real-world deployment continues to...
Background: Mapping anti–HLA class I cross-reactivity from single-antigen bead (SAB) mean fluorescence intensity (MFI) data supports donor selection. However, interpretation is complicated by analytical choices and assay variability. Methods: A...
Internet Gaming Disorder (IGD), recognized in the International Classification of Diseases (ICD-11), affects millions—especially adolescents and young adults—and poses challenges that invite scalable innovations in care. This narrative re...
Within the framework of the ongoing development of application of Machine Learning models in Medicine and Physical Therapy, the development of accurate prognosis algorithms for postoperative patients during the rehabilitation phase remains an area re...
The exponential growth of unstructured medical data, particularly clinical notes and diagnostic reports, presents mounting challenges for healthcare knowledge extraction and utilization. This study introduces the Digital Medical Catalog (DMC), a fram...
Class imbalance in clinical electrocardiogram (ECG) datasets limits the diagnostic sensitivity of automated arrhythmia classifiers, particularly for rare but clinically significant beat types. We propose a three-stage hybrid generative pipeline that...
Coronary artery disease (CAD) is the leading cause of death worldwide, highlighting the need for more reliable and efficient diagnostic tools beyond conventional methods. Artificial intelligence (AI), particularly machine learning (ML) and deep learn...
Wearable sleep monitoring systems provide a scalable and low-burden alternative to laboratory-based polysomnography, but overnight physiological recordings collected from wearable sensors are frequently corrupted by poor skin contact, sensor displace...
Digital twins (DTs) are dynamic, virtual representations of individual patients that could support predictive diagnostics and personalized therapeutic optimization. Their development depends on patient-level data from real-world data (RWD) sources an...
Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, underscoring the importance of early and accurate diagnosis to improve patient outcomes. Magnetic resonance imaging (MRI) is a highly sensitive imaging...
Gastrointestinal (GI) adverse drug reactions (ADRs) are common among glucagon-like peptide-1 receptor agonist (GLP-1 RA) users and frequently contribute to treatment discontinuation and reduced therapeutic benefit. This retrospective study aimed to d...
This study developed a two-stage, explainable machine learning framework to predict 18-month MMSE-based cognitive status from baseline multimodal data in community-dwelling older adults in Japan. A hierarchical design was used in which Stage 1 distin...
Background: Complete blood count (CBC) phenotypes are routinely assigned using deterministic rule-based thresholds. While operationally efficient, such rules may lead to unstable phenotype assignments for results close to clinical cutoffs in the pres...
Biomedical spectroscopy, including Raman, surface-enhanced Raman spectroscopy (SERS), infrared spectroscopy, and hyperspectral imaging, is increasingly combined with machine learning for disease classification, sample characterization, and biomarker-...
The increasing challenges of healthcare systems demand innovative approaches to resource optimization, particularly for hospitals operating under economic and operational constraints. This study investigates the organizational and managerial factors...
Glucocorticoid-induced hypertension affects over 30% of treated patients, yet its underlying mechanisms remain unclear, particularly how glucocorticoids regulate renin within the renin-angiotensin-aldosterone system (RAAS). Modeling these dynamics is...
This study introduces a novel approach for optimizing models that reconstruct high-quality full-dose bone scintigraphy images from their 40% low-dose counterparts using optimized attention-based U-Net architectures. We utilized Genetic and Epigenetic...
Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, highlighting the need for computational tools that can help resolve overlapping phenotypes and support timely, mechanism-informed in...
(1) Background: Ultrasound (US) imaging is widely used in clinical diagnosis but is often degraded by speckle noise, which reduces image quality and can hinder interpretation. Deep learning (DL) has emerged as a promising approach for US denoising, y...
Background: Chest radiography is widely used in clinical workflows; however, exploratory image-level classification across multiple public-dataset categories remains less studied than single-disease classification tasks. We aimed to develop and inter...
Distinguished colleagues in the global medical and AI communities, [...]
Artificial intelligence foundation models are increasingly deployed for prostate cancer Gleason grading, where GP3/GP4 distinction directly impacts treatment decisions (active surveillance vs. intervention). However, these models may achieve high val...
