Advances in digital technology and the coronavirus disease (COVID-19) pandemic have accelerated the digital transformation of healthcare. Digital therapeutics (DTx), which deliver evidence-based interventions through digital means to treat or prevent diseases, are expected to generate significant value in modern healthcare. Strategic intellectual property (IP) protection for DTx is essential to support development costs, including […]
Correction: Artificial intelligence assessment of valvular disease and ventricular function by a single echocardiography view
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Comparative performance of ChatGPT-5 and DeepSeek on the Chinese ultrasound medicine senior professional title examination
BackgroundLarge language models (LLMs) have shown growing potential for medical education and assessment, but evidence on their performance in specialty certification exams in China—particularly in ultrasound medicine—remains limited.ObjectiveTo compare the performance of ChatGPT-5 and DeepSeek on the Chinese Ultrasound Medicine Senior Professional Title Examination, overall and by item type.MethodsBetween August and September 2025, we randomly […]
Editorial: Ethical considerations of large language models: challenges and best practices
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Depression detection using deep learning and large language models from multimodalities
Depression is a complex psychiatric disorder that affects neural functioning, cognition, emotion, and behavior, making objective assessment a persistent clinical challenge. Traditional diagnostic methods depend on subjective interpretation, whereas recent advances in deep learning have enabled automated, data-driven detection across physiological and behavioral modalities. Among unimodal approaches, electroencephalography (EEG) remains the most widely used due […]
Unlocking electronic health records: a hybrid graph RAG approach to safe clinical AI for patient QA
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 […]
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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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 […]
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 […]
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 […]