IntroductionElectronic health record (EHR) systems present clinicians with vast repositories of clinical information, creating a significant cognitive burden where critical details are easily overlooked. While Large Language Models (LLMs) offer transformative potential for data processing, they face significant limitations in clinical settings, particularly regarding context grounding and hallucinations. Current solutions typically isolate retrieval methods, focusing […]
Virtual reality in treatment of psychological disorders: a systematic review
ObjectiveThe paper aims to systematically review the literature on the efficacy of virtual reality (VR) based therapies to treat mental health disorders in Randomized Control Trials (RCTs).MethodsAs of January 2,025, three databases were searched using relevant key terms (PsycINFO, PubMed, and Medline) and Rayyan tool. Eligible studies were English-language RCTs of VR-based interventions with a […]
Toward terminological clarity in digital biomarker research
Digital biomarker research has generated thousands of publications demonstrating associations between sensor-derived measures and clinical conditions, yet clinical adoption remains negligible. We identify a foundational problem: the field lacks consensus on what constitutes a digital biomarker, applying identical terminology to direct physiological measurement (continuous glucose monitoring), algorithmic prediction of biological substrates (voice analysis for dopaminergic […]
Trust and anxiety as primary drivers of digital health acceptance in multiple sclerosis: toward an extended disease-specific technology acceptance model
BackgroundDigital health applications and AI-supported wearables may benefit people with Multiple Sclerosis (MS), yet fluctuating cognitive and physical symptoms could shape adoption in ways not fully captured by traditional acceptance models.ObjectiveTo identify determinants of digital health acceptance in MS, focusing on emotional factors and disease-related moderators, and to compare these patterns with individuals living with […]
How physicians embrace AI: insights from technology acceptance and trust theories
ObjectiveThis study investigates the factors influencing physicians’ acceptance and adoption of artificial intelligence (AI) technologies in clinical practice, integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), while also examining the mediating role of trust.MethodsA structured survey was conducted among 414 physicians assessing their perceptions of AI technologies using constructs from […]
Through the looking glass: ethical considerations regarding LLM-induced hallucinations to medical questions
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Real-world federated learning for brain imaging scientists
BackgroundFederated learning (FL) has the potential to boost deep learning in neuroimaging but is rarely deployed in real-world scenarios, where its true potential lies. We propose FLightcase, a new FL toolbox tailored for brain research, and evaluate it on a real-world FL network to predict the cognitive status in patients with multiple sclerosis (MS) from […]
Translating AI research into reality: summary of the 2025 voice AI Symposium and Hackathon
The 2025 Voice AI Symposium represented a transition from conceptual research to clinical implementation in vocal biomarker science. Hosted by the NIH-funded Bridge2AI-Voice consortium, the meeting convened global experts to address the methodological, ethical, and translational challenges of integrating voice-based artificial intelligence (AI) into healthcare. This mini-review synthesizes symposium insights across six domains: multimodal integration, […]
Correction: Feasibility and reliability of a smartwatch to detect atrial fibrillation after cardiac surgery: a prospective study
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BioMarin halts Voxzogo trials due to safety signal
BioMarin has suspended dosing in phase 2 trials of its achondroplasia therapy Voxzogo aimed at broadening the drug’s label.
How hospital accreditation requirements bridge enablers for AI readiness: interpretative analysis of intersections in framework standards
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Correction: Adopting machine learning to predict breast cancer patients adherence with lifestyle recommendations and quality of life outcomes
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