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, […]
Decoding perceived risks in online healthcare services: a safety–trust model based on grounded theory
IntroductionThe rapid rise of online healthcare services (OHSs) in China has improved access to medical information and services while creating new uncertainties related to quality, security, and trust. This study aims to deepen the understanding of perceived risk in OHSs and provide empirical guidance for digital health governance, patient safety strategies, and the development of […]
Anonymization, accountability, and access: legal dimensions of health data sharing in federated networks. Perspectives from empirical study
This paper explores the perspectives of stakeholders involved in federated networks for health data sharing, focusing on the legal and practical dimensions of data protection and governance under GDPR and EHDS in the development of such infrastructures. Using a qualitative approach centered on perspectives of 19 experts with experience in projects building federated networks, it […]
AI-enabled cardiovascular devices: a lifecycle playbook for evidence, change control, and post-market assurance
AI-enabled cardiovascular devices are increasingly used in imaging, physiological signal analysis, and clinical decision support systems. Despite growing clinical adoption, requirements for evidence generation, software change management, and post-deployment assurance remain fragmented across jurisdictions and are often difficult to translate into operational processes within healthcare organizations. This review synthesizes common foundations of software as a […]
From bedside to bytes: the digital transformation of the healthcare workforce
Digital transformation is reshaping healthcare work, whereas research on workforce implications remains fragmented across disciplines. Effects like burnout, resistance, and workflow disruption are often framed as implementation failures rather than systematic outcomes of how work is reorganized. This Mini Review advances a four-dimensional analytical lens distinguishing work execution (task distribution, sequencing, temporal organization), work experience […]
Chatbots as frontline educators in sexual reproductive health rights: evidence, limitations, and ethical considerations
Chatbots are increasingly used in digital health to expand access to information and support user engagement. In sexual and reproductive health and rights (SRHR), where stigma, privacy concerns, and health system constraints often limit timely access to accurate information, chatbots have been proposed as scalable tools for delivering education and facilitating service navigation. This perspective […]
Structuring integration for patient-centered care: a review-informed ontology-driven modular front-end framework for digital health innovation
BackgroundSemantic interoperability remains a significant barrier in healthcare, particularly when integrating patient-reported, clinical, and genomic data to enable personalized care. Existing models rarely focus on patient-centered, ontology-driven front-end architectures based on widely adopted standardized medical ontologies and terminologies. Within broader Personal Health Data Space (PHDS) initiatives, such integration increasingly depends on front-end frameworks that enable […]
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 […]
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 […]
Through the looking glass: ethical considerations regarding LLM-induced hallucinations to medical questions
Post Content
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 […]