This mini review examines the emerging use of digital platforms for ethics review (DPER) and the extent to which research ethics committees (RECs) are prepared for digital transformation and operational autonomy. Drawing on a structured analysis of peer-reviewed studies published between 2013 and 2024, the review synthesises the existing research on DPER and examines reported […]
Recognition and linking of discontinuous named entities in healthcare: a comparative performance analysis
IntroductionThe recognition and linking of discontinuous named entities (DiscNEs) in healthcare remain challenging due to their fragmented structure and semantic complexity. This study presents a comparative analysis of two state-of-the-art DiscNER models: TriG-NER, a grid-tagging architecture, and DocDiscNER, a generative document-level model. The aim is to provide a broader understanding of their generalisation capabilities, performance […]
Performance of large language models in delivering accurate and comprehensible patient information on heart failure and cardiomyopathy
BackgroundLarge language models (LLMs) are increasingly used by patients seeking cardiovascular health information through digital platforms. However, their accuracy and suitability for providing guidance on heterogeneous diseases such as cardiomyopathies and heart failure remain inadequately evaluated. This study systematically benchmarked state-of-the-art LLMs on patient-oriented heart failure and cardiomyopathy queries regarding clinical appropriateness and comprehensibility.MethodsSix prominent […]
Personalized vs. population-based speech models for multi-dimensional mental health prediction
IntroductionMental disorders such as depression, anxiety, and stress are increasingly prevalent, particularly among young adults. Traditional assessment methods rely on self-reports and resource-intensive clinician interviews, limiting scalability and accessibility. Speech-based machine learning models offer a scalable and non-invasive alternative; however, population-level models often struggle to distinguish disorder-related signals from speaker-specific traits, reducing individual prediction accuracy.MethodsWe […]
Dynamic consent framework for low-dose CT scan lung cancer screening: autonomy, privacy, ethical data management
ObjectivesTo develop and implement a blockchain-based dynamic consent framework integrated with artificial intelligence (AI) to support Low-Dose Computed Tomography (LDCT) lung cancer screening and biobank data utilization in Taoyuan, Taiwan.MethodsWe designed a Web 3.0–based dynamic consent platform that enables participants in the Taoyuan Expanded Lung Cancer Screening Program to manage and update their consent preferences […]
Artificial intelligence in undergraduate medical education clinical skills curricula: a scoping review of implementations since 2022
PurposeTo systematically identify and synthesize peer-reviewed literature describing implemented AI innovations within undergraduate medical education clinical skills curricula from January 2022 through January 2026.MethodThe authors conducted a scoping review querying PubMed and Scopus, supplemented by SciSpace as an AI-assisted citation discovery tool. Eligible studies described utilizing AI to deliver the clinical skills curriculum in innovative […]
Mental health app crisis support assessment framework: development and pilot testing
Mental health applications increasingly serve as stand-alone interventions or adjuncts to clinical care, yet their capacity to support users experiencing acute psychological distress remains poorly characterized. This study introduces the Mental Health App Crisis Support Assessment Framework (MHACSAF), a structured instrument for evaluating crisis support implementation in mental health apps, and reports findings from its […]
The sound of engagement: assessing the feasibility and acceptability of an AI-generated personalized podcast as a between-session resource for therapy
IntroductionConsistent client engagement with between-therapy session activities (i.e., homework) is a strong predictor of positive psychotherapy outcomes. Because traditional homework activities aren’t always tailored to the individual, they can fail to provide the support patients need to translate session insights into real-life change. The integration of digital tools into weekly psychotherapy presents an opportunity to […]
Reducing bias and enhancing equity in AI-enabled precision nutrition: addressing measurement error across wearables, multiomics, and dietary data
Artificial intelligence (AI) can offer individualized dietary guidance based on multimodal data collected from various sources, including wearable sensors, high-dimensional multiomics and biomarker analyses, behavioral tracking, and self-reported dietary intake, enabling the emergence of precision nutrition. However, the predictive power and fairness of these models rely on the quality of the data inputs, and measurement […]
Digital phenotyping of affect and stress in emerging adults
BackgroundDepression is highly heterogeneous and difficult to monitor or predict in daily life. One strategy for monitoring depressive symptoms is digital phenotyping, the real-time tracking of behaviors via personal devices. Digital phenotyping may be especially useful for predicting mood in emerging adults, a developmental period characterized by heightened rates of depression and smartphone use. However, […]
Digital marketing of e-cigarettes in Southeast Asia: a neglected digital health and platform governance challenge for youth protection
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Editorial: Advances in generative artificial intelligence for mental health
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