JMIR Medical Informatics

Health care systems face growing fiscal pressure while AI reaches clinical parity in several domains. UK National Health Service expenditure rose by 52%, while Australia's health expenditure grew by 29% between 2019 and 2023. Yet large-scale cost savings from AI remain limited, largely because implementation constraints continue to outweigh technical capability.

Accurate preanesthetic assessment is essential for perioperative risk stratification, but conventional tools such as the American Society of Anesthesiologists (ASA) physical status classification and postoperative nausea and vomiting (PONV) risk scores may be affected by subjective judgment, incomplete documentation, and fragmented clinical data. Large language models may support preanesthetic assessment by integrating structured and unstructured clinical information.

Rare eye diseases are characterized by low prevalence, clinical heterogeneity, and fragmented data collection, which limit the reliability of analysis results and multicenter research. In France, the development of health data warehouses is strictly regulated by national data protection authorities. While international initiatives aim to harmonize rare disease registries, capturing hyperspecialized, multimodal clinical records and ophthalmology-specific imaging within a fully compliant and sustainable infrastructure remains a major operational challenge.

Public reporting systems (PRSs) in health care are defined as digital platforms that make comparative health care performance data available to the public (eg, Hospital Compare in the United States, NHS Choices in the United Kingdom, national quality registries in Europe, and MyHospital in Australia). These systems aim to improve transparency, accountability, and patient choice. While these systems have been widely studied from policy and clinical perspectives, the information system (IS) foundations that enable their operation, including architecture, interoperability, data governance, APIs, usability, and technical performance, remain underexplored.

Acute chest pain (ACP) is one of the most common chief complaints in the emergency department (ED), accounting for approximately 8% of all ED visits. However, among patients presenting with chest pain suggestive of cardiac origin, fewer than 10% are ultimately diagnosed with acute coronary syndrome (ACS).

A persistent discrepancy exists between patient-reported information and physician documentation. While conversational agents have been developed to collect medical histories prior to consultations, existing evaluations have largely focused on diagnostic accuracy or user satisfaction rather than on the completeness and clinical relevance of the information collected. There remains a need to assess the extent to which clinically relevant information is captured through chatbot-based interviews, and to understand how model configurations and instructional strategies influence this coverage.

The clinical outcomes of diffuse large B-cell lymphoma (DLBCL) are highly heterogeneous. While clinical indices like the international prognostic index (IPI) are widely used, their predictive accuracy remains limited. The integration of molecular features with clinical characteristics holds promise for developing more precise prognostic models to improve risk stratification and personalize treatment strategies.

Atrial fibrillation (AF) is a common arrhythmia associated with an increased risk of stroke and heart failure. To improve prevention, recent studies have used deep learning models to identify at-risk individuals early from normal sinus rhythm (NSR). However, studies using mobile electrocardiogram (mECG) in outpatient, real-world settings remain underexplored.

Large language models (LLMs) are rapidly evolving from text-based agents to multimodal systems capable of interpreting medical images. While their textual reasoning has improved, the safety implications of this shift remain underexplored, specifically regarding the alignment between visual interpretation and textual advice in low back pain (LBP) management.

Despite advances in understanding and treating non–ST-elevation acute coronary syndrome (NSTE-ACS), patients continue to experience high rates of adverse outcomes, particularly those with non–ST-segment elevation myocardial infarction, which remains a leading cause of cardiovascular mortality. Existing risk models may not fully reflect contemporary patient populations due to substantial changes in clinical profiles. Developing new machine learning (ML)–based risk calculators may improve the prediction of in-hospital mortality (IHM) at different stages of the diagnostic process, and ultimately improve patient outcomes.

Screening for atherosclerosis is essential for early intervention, but conventional screening methods are often invasive and resource-intensive. As a result, there is growing interest in leveraging AI with noninvasive tools such as retinal fundus imaging to enable opportunistic cardiovascular risk assessment. The deep-learning funduscopic atherosclerosis score (DL-FAS) is an AI-derived biomarker, generated by a deep learning model, that was developed in a previous study to reflect the likelihood of carotid artery atherosclerosis from retinal fundus images.