JMIR Medical Informatics

Misspellings in medication names can compromise patient safety, reduce data utility, and impede large-scale data initiatives that integrate medication information from electronic health records (EHRs). Existing methods for detecting misspelled medical terms are mostly dictionary-based and can lead to high false-positive rates when correctly spelled but previously unseen (out-of-vocabulary) terms are encountered.

Patient safety events (PSEs) are preventable incidents that cause, or have the potential to cause, harm to patients during their medical journey. Although incident reporting systems capture large volumes of such events, only a small proportion undergo comprehensive investigation due to the resource-intensive nature of the review process. Patient safety specialists are tasked with triaging PSEs to prioritize high-severity cases that warrant timely institutional investigation; however, the rapidly increasing volume of events has made manual triage increasingly impractical.

AI-enabled voice electronic medical records (EMRs) are increasingly promoted as tools to reduce clinician documentation burden; however, empirical evidence from multilingual, resource-constrained health systems in sub-Saharan Africa remains limited.

Although Digital Imaging and Communications in Medicine (DICOM) metadata are widely used to manage medical imaging data and support clinical workflows, their suitability as a sole basis for automatic computed tomography (CT) series labeling and characterization is limited. DICOM metadata are frequently inconsistently populated, institution specific, use unregulated private tags, and have variable reliability even within standardized fields. Consequently, automated series selection for downstream AI applications often remains unreliable, necessitating manual curation within clinical workflows.

Systematic strategies to harness electronic health record (EHR) workflows, reduce redundancy, and support decision-making remain limited in acute care surgery (ACS). Understanding how EHR systems and workflows intersect with time-sensitive settings is critical to improving decision-making and outcomes in ACS.

The World Health Organization advises that every nation should take responsibility for guaranteeing access to health care services as a basic human right. However, due to financial constraints and geographical hurdles, only around half of the population in Africa has access to contemporary health care services.

Controlled substances require strict life cycle management, but manual workflows can lead to documentation errors, traceability gaps, and delays in empty-ampoule recovery.

Digital therapeutics (DTx) are emerging as evidence-based software interventions, but current AI-driven personalization approaches lack dedicated safety-focused frameworks and face challenges due to scarce long-term outcome data and unpredictable model behaviors. We propose SAFE_DTx, a safety-first architectural framework for DTx that integrates well-established principles of predictive modeling and constrained decision-making to prioritize patient safety. SAFE_DTx’s 2-module architecture comprises an AI feedback prediction module that forecasts short-term patient responses and a constrained planning module that selects the next intervention under explicit safety constraints. By decoupling these components and enforcing clear safety guardrails, the framework enables dynamic, real-time adaptation to individual patient feedback while staying within evidence-based safety limits. This modular design also enhances transparency in the decision-making process, and an in silico evaluation demonstrates its preliminary architectural feasibility, showing greater engagement and no safety violations compared to baseline strategies within the simulated environment. SAFE_DTx’s safety-by-design architecture aligns with emerging regulatory emphasis on AI transparency and patient safety. It directly addresses key clinical challenges in AI-driven DTx personalization by ensuring that tailored interventions do not compromise patient safety.

The integration of large language models (LLMs) into high-risk systems such as health care is accelerating. Rigorous evaluations aligned with emerging legislation are imperative prior to their incorporation into university educational platforms and clinical practice settings.

Due to the heterogeneity of symptom terminology and the lack of industry standards, the same symptom is often described using multiple expressions. Current normalization approaches struggle to comprehensively retrieve standard terms when a raw term maps to multiple symptoms.

Traditional Chinese medicine (TCM) constitution is a structured health state taxonomy used in preventive care, but its relationship with routinely collected health examination data, disease-related markers, and longitudinal change remains difficult to interpret in clinical informatics settings.

Liposuction is widely performed to remove localized fat deposits and improve body contour, yet individualized prediction of postoperative outcomes remains challenging. Existing machine learning (ML) studies have largely focused on single-outcome prediction, with limited attention to the interdependence between postoperative body weight and circumferential size.