Digital health technology (DHT) tools for Parkinson’s disease (PD) e.g., smartphones and wearables were used for remote and frequent measurement of motor signs in the phase 2 PASADENA study of the anti-alpha-synuclein monoclonal antibody…
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As digital biomarkers gain traction in Alzheimer’s disease (AD) diagnosis, understanding recent advancements is crucial. This review conducts a bibliometric analysis of 431 studies from five online databases: Web of Science, PubMed, Embase, IEEE…
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Combined immunodeficiencies (CID) and common variable immunodeficiencies (CVID), prevalent yet substantially underdiagnosed primary immunodeficiencies, necessitate improved early detection. Leveraging large-scale electronic health records (EHR) from four nationwide US cohorts, we developed a novel causal…
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The ethical integration of artificial intelligence (AI) in healthcare necessitates addressing fairness. AI fairness involves mitigating biases in AI and leveraging AI to promote equity. Despite advancements, significant disconnects persist between technical solutions and…
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The widespread adoption of Electronic Health Records (EHRs) and deep learning, particularly through Self-Supervised Representation Learning (SSRL) for categorical data, has transformed clinical decision-making. This scoping review, following PRISMA-ScR guidelines, examines 46 studies published…
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Subjective cognitive decline (SCD) is a potential early marker of neurodegeneration, with negative affective states such as depression and anxiety significantly contributing to cognitive impairment. Digital treatments show promise, yet evidence of their use…
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Ambient digital scribing (ADS) tools alleviate clinician documentation burden, reducing burnout and enhancing efficiency. As AI-driven ADS tools integrate into clinical workflows, robust governance is essential for ethical and secure deployment. This study proposes…
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Accurate detection of Parkinson’s disease (PD) through speech analysis holds great promise for early diagnosis and improved patient management. However, developing robust machine learning models is challenging due to the decentralized nature of medical…
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Regulatory agencies, such as the European Commission and the U.S. Food and Drug Administration, are now permitting electronic instructions for use (eIFUs) to be distributed alongside paper instructions for use (IFUs) for medical devices….
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We explore biases present in publicly available fetal ultrasound (US) imaging datasets, currently at the disposal of researchers to train deep learning (DL) algorithms for prenatal diagnostics. As DL increasingly permeates the field of…
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