JMIR Medical Informatics

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a leading cause of chronic disease and can deteriorate into liver fibrosis or hepatocellular carcinoma. Each of its subtypes—obesity, diabetics, and lean—is associated with divergent fibrosis burdens and complications, yet existing analytics methods often overlook its multisystem nature, intra-phenotype heterogeneity, and disease dynamics. These limitations hinder accurate risk stratification and restrict personalized intervention planning.

Acute care utilization (ACU) represents a major economic burden in oncology, which can ideally be prevented. Existing models effectively predict such events.

Clinical trial eligibility screening using electronic medical records (EMRs) is challenging due to the complexity of patient data and the varied clinical terminologies. Manual screening is time-consuming, requires specialized knowledge, and can lead to inconsistent participant selection, potentially compromising patient safety and research outcomes. This is critical in time-sensitive conditions like acute ischemic stroke. While computerized clinical decision support tools offer solutions, most require software engineering expertise to update, limiting their practical utility when eligibility criteria change.

Efforts are being made to integrate digital health technologies into clinical care for multiple sclerosis (MS), to improve patient monitoring. Efficiently probing how they might impact clinical care could streamline and focus digital tool development. The Floodlight digital tool, comprising 5 smartphone sensor–based tests, was used to generate health-related data on patient function and symptoms in a clinical simulation.

The use of large language models (LLMs) in radiology is expanding rapidly, offering new possibilities in report generation, decision support, and workflow optimization. However, a comprehensive evaluation of their applications, performance, and limitations across the radiology domain remains limited. Objective: To map current applications of LLMs in radiology, evaluate their performance across key tasks, and identify prevailing limitations and directions for future research.

Liver failure often results in significant coagulation dysfunction, which is a major complication. Artificial liver support systems (ALSS) have been used to ameliorate coagulation parameters, but the dynamic nature of these improvements and the development of predictive models remain insufficiently explored.

This Author Reply addresses the comments raised in response to our Viewpoint, “Digital Health Innovations to Catalyze the Transition to Value-Based Health Care.” We appreciate the thoughtful reflections that emphasize the broader system-level requirements for implementing value-based health care (VBHC) beyond technology adoption. In this response, we affirm the importance of patient co-design, digital equity, data interoperability, and workforce education as essential enablers of VBHC. We also acknowledge the significance of sustainable financing models and stakeholder collaboration to support long-term transformation. We concur that realizing VBHC requires systemic alignment across governance, clinical, and financial dimensions, and we support continued interdisciplinary research to guide equitable and scalable digital health innovation.

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Depressive episodes in bipolar disorder are frequent, prolonged, and contribute substantially to functional impairment and reduced quality of life. Therefore, early and objective detection of bipolar depression is critical for timely intervention and improved outcomes. Multimodal speech analyses hold promise for capturing psychomotor, cognitive, and affective changes associated with bipolar depression.

Sepsis-associated encephalopathy (SAE) represents a critical complication of sepsis, especially among older adults. Despite its clinical relevance, there remains a lack of accessible and practical tools specifically designed to predict 28-day mortality in this vulnerable population.

Falls among hospitalized patients are a critical issue that often leads to prolonged hospital stays and increased health care costs. Traditional fall risk assessments typically rely on standardized scoring systems; however, these may fail to capture the complex and multifactorial nature of fall risk factors.

Reusing long-term data from electronic health records is essential for training reliable and effective health artificial intelligence (AI). However, intrinsic changes in health data distributions over time—known as dataset shifts, which include concept, covariate, and prior shifts—can compromise model performance, leading to model obsolescence and inaccurate decisions.