JMIR Medical Informatics

Noninvasive brain stimulation may alleviate social media addiction, but its efficacy requires accurate individual targeting and real-time brain monitoring. The neural mechanisms underlying the effects of social media use (SMU) remain unclear, limiting the development of interventions. Understanding how different levels of SMU modulate brain activity could guide personalized neuromodulation strategies.

Recent crises involving zoonotic diseases (Ebola, COVID-19, and Mpox) have highlighted the limitations of fragmented public health systems for the prevention and response to health emergencies at the international and continental level, and particularly in Africa, more specifically in the Democratic Republic of the Congo (DRC). In this context, the One Health (OH) approach and its extension, One Digital Health (ODH), articulated with the findable, accessible, interoperable, reusable (FAIR) principles, offer a framework for rethinking the digital transition in health in the DRC. This study analyzes the reality of this transition in Kinshasa, DRC, and questions the feasibility of ODH in this context.

Transfusion thresholds in upper gastrointestinal bleeding are debated; hemoglobin cutoffs of 70‐80 g/L are widely cited yet inconsistently applied. Common risk scores offer limited individualized guidance and rarely provide calibrated, interpretable predictions for transfusion decisions.

Pneumonia is a common infectious disease, and antibiotic treatment in hospitalized patients must balance efficacy, safety, and resistance risk. However, antibiotic selection and dose adjustment still rely heavily on clinician experience. Although large language models (LLMs) are promising for clinical reasoning, their direct use for antibiotic selection and dose recommendation is limited by hallucinations and weak adherence to clinical constraints.

Online health care consultation provides patients with broad access to physicians, but also presents the challenge of selecting a suitable doctor in the absence of triage guidance. Patients have multifaceted needs, prioritizing not only recommendation accuracy but also physician service quality, and diversity of physician expertise. Furthermore, the sparsity of patient interaction data intensifies the difficulty of providing balanced and effective recommendations.

Postoperative nausea and vomiting are common complications after anesthesia. However, vomiting represents a clinically distinct and objectively measurable endpoint.

Software-based and AI-enabled medical devices are increasingly networked and updatable, expanding the attack surface and making cybersecurity governance intersect with quality management and postmarket oversight. Regulated device risk management nevertheless remains primarily oriented toward patient-safety harms under ISO 14971 frameworks, which may not fully capture cybersecurity risks affecting data integrity, system resilience, or service continuity.

Clinical retrieval-augmented generation depends on embedding models. A companion study found that non–retrieval-trained encoders underperformed retrieval-trained general-purpose embeddings and produced near-degenerate embedding geometry, but did not localize the architectural origin, separate training-domain from training-objective effects, or test whether the degradation can be corrected without retraining.

In simulated English and Spanish clinical encounters, ambient AI scribes propagated interpreter errors into clinical notes, with patterns varying by speaker role and error type. These findings highlight the need for further evaluation of AI-scribe performance in multilingual and interpreter-mediated clinical care.

In a single US health system, fewer than one in three adults flagged by spinal muscular atrophy–associated diagnostic codes had molecularly confirmed spinal muscular atrophy (positive predictive value 27%), with the majority miscoded across clinically distinct categories.

Artificial intelligence–generated health information is increasingly used by patients, but its reliability, visible transparency indicators, and readability remain uncertain in specialized ophthalmic conditions such as age-related macular degeneration (AMD).

Health information technology (HIT) interventions are complex, context-dependent, and often insufficiently theorized, which can hinder their design, implementation, and evaluation. Program theory approaches such as logic models and theory of change (ToC) are well-established in public health and implementation science for articulating causal assumptions. Their use in medical informatics, however, appears inconsistent. A systematic overview of how logic models and ToC have been applied to HIT interventions is therefore needed to support theory-informed development and cumulative learning in the field.