JMIR Medical Informatics

Frequent vital sign (VS) monitoring is central to inpatient safety but is traditionally performed manually every 4 hours, a century-old practice that can miss early clinical deterioration, disrupt patient sleep, and impose a heavy documentation burden on nursing staff. Continuous VS monitoring (CVSM) using wearable remote patient monitoring devices enables near real-time, high-frequency VS measurement while reducing manual workload and preserving patient rest.

Emergency triage accuracy is critical but varies with clinician experience, cognitive load, and case complexity. Mis-triage can delay care for high-risk patients and exacerbate crowding through unnecessary prioritization. Large language models (LLMs) show promise as triage decision-support tools but are vulnerable to hallucinations. Retrieval-augmented generation (RAG) may improve reliability by grounding LLM reasoning in authoritative guidelines and real clinical cases.

Early-life health risks can shape long-term morbidity trajectories, yet prevailing pediatric risk assessment paradigms are often fragmented and insufficiently capable of integrating heterogeneous data streams into actionable, individualized profiles.

Quantitative magnetic resonance imaging (qMRI) is an advanced technique that can map the physical properties (T1, T2 and proton density (PD)) of different tissues, offering crucial insights for disease diagnosis. Nonetheless, the practical application of this technology is indeed constrained by several factors, with the most notable being the protracted scanning duration.

Given the highly biological heterogeneity of breast cancer, a more effective non-invasive diagnostic tool that unravels microscopic histopathology patterns is of urgent need.

Deep learning models have shown strong potential for automated fracture detection on medical images. However, their robustness under varying image quality remains uncertain, particularly for small and subtle fractures such as scaphoid fractures. Understanding how different types of image perturbations affect model performance is crucial for ensuring reliable deployment in clinical practice.

Opioids are a widely prescribed class of medication for pain management. However, they have variable efficacy and adverse effects among patients, due to complex interplay between biological and clinical factors. Pharmacogenetic (PGx) testing can be utilized to match patients’ genetic profiles to individualize opioid therapy, improving pain relief and reducing the risk of adverse effects. Despite its potential, PGx uptake (utilization of PGx testing) remains low due to a range of barriers at the patient, health care provider, infrastructure, and financial levels. Since testing typically involves a shared decision between the provider and patient, predicting likelihood of patient undergoing PGx testing and understanding the factors influencing that decision can help optimize resource use and improve outcomes in pain management.

When used correctly, electronic medical records (EMRs) can support clinical decision-making, provide information for research, facilitate coordination of care, reduce medical errors, and generate patient health summaries. Studies have reported large differences in the quality of EMR data.

There is a lack of venous thromboembolism (VTE) risk prediction models based on gene expression information.

Parkinson disease (PD) presents diagnostic challenges due to its heterogeneous motor and nonmotor manifestations. Traditional machine learning (ML) approaches have been evaluated on structured clinical variables. However, the diagnostic utility of large language models (LLMs) using natural language representations of structured clinical data remains underexplored.

In recent years, the incidence of cognitive diseases has also risen with the significant increase in population aging. Among these diseases, Alzheimer’s disease constitutes a substantial proportion, placing a high-cost burden on healthcare systems. To give early treatment and slow the progression of patient deterioration, it is crucial to diagnose Mild Cognitive Impairment (MCI), a transitional stage.

Artificial intelligence (AI) offers potential solutions to address the challenges faced by a strained mental healthcare system, such as increasing demand for care, staff shortages and pressured accessibility. While developing AI-based tools for clinical practice is technically feasible and has the potential of producing real-world impact, only few are actually implemented into clinical practice. Implementation starts at the algorithm development phase, as this phase bridges theoretical innovation and practical application. The design and the way the AI tool is developed may either facilitate or hinder later implementation and use.