JMIR Medical Informatics

This research letter summarizes early lessons from 4 enterprise implementations of artificial intelligence–enabled customer relationship management platforms in health care and describes governance practices associated with improvements in affordability, adherence, and access at program level.

Artificial intelligence tools, particularly large language models (LLMs), have shown considerable potential across various domains. However, their performance in the diagnosis and treatment of breast cancer remains unknown.

Venous thromboembolism (VTE) is a common and severe complication in intensive care unit (ICU) patients with sepsis. Conventional risk stratification tools lack sepsis-specific features and may inadequately capture complex, nonlinear interactions among clinical variables.

We used the free artificial intelligence (AI) tool Google NotebookLM, powered by the large language model Gemini 2.0, to construct a medical decision-making aid for diagnosing and managing airway diseases and subsequently evaluated its functionality and performance in a clinical workflow. After feeding this tool with relevant published clinical guidelines for these diseases, we evaluated the feasibility of the system regarding its behavior, ability, and potential, and we created simulated cases and used the system to solve associated medical problems. The test and simulation questions were designed by a pulmonologist, and the appropriateness (focusing on accuracy and completeness) of AI responses was judged by 3 pulmonologists independently. The system was then deployed in an emergency department setting, where it was tested by medical staff (n=20) to assess how it affected the process of clinical consultation. Test opinions were collected through a questionnaire. Most (56/84, 67%) of the specialists’ ratings regarding AI responses were above average. The interrater reliability was moderate for accuracy (intraclass correlation coefficient=0.612; P<.001) and good on completeness (intraclass correlation coefficient=0.773; P<.001). When deployed in an emergency department (ED) setting, this system could respond with reasonable answers, enhance the literacy of personnel about these diseases. The potential to save the time spent in consultation did not reach statistical significance (Kolmogorov-Smirnov [K-S] D=0.223, P=.24) across all participants, but it indicated a favorable outcome when we analyzed only physicians’ responses. We concluded that this system is customizable, cost efficient, and accessible to clinicians and allied health care professionals without any computer coding experience in treating airway diseases. It provides convincing guideline-based recommendations, increases the staff’s medical literacy, and potentially saves physicians’ time spent on consultation. This system warrants further evaluation in other medical disciplines and health care environments.

A rapidly aging population led to an increase in the number of patients with chronic diseases and polypharmacy. Although investigations on the appropriate number of drugs for older patients have been conducted, there is a shortage of studies on polypharmacy criteria in older inpatients from multiple institutions.

Rib fractures are present in 10–15% of thoracic trauma cases but are often missed on chest radiographs (CXRs), delaying diagnosis and treatment. Artificial intelligence (AI) may improve detection and triage in emergency settings.

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.