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The Lancet: Digital Health
Scopus
ISSN 2589-7500 · Elsevier · 335 artikel di indeks
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Artikel Jurnal
Performance and label efficiency of traditional deep-learning models and a retina-specific foundation model for ocular and systemic disease detection: a retrospective comparative study
Summary: Background: RETFound, a self-supervised retina-specific foundation model, has shown potential in downstream tasks, but its performance in comparison with that of traditional deep-learning models remains unclear. We aimed to evaluate RETFound against three supervised deep-learning models…
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Artikel Jurnal
Multimodal artificial intelligence-based long-term mortality prediction after transcatheter aortic valve implantation: a multicentre development, validation, and testing study
Summary: Background: Appropriate risk prediction is essential to inform long-term management in patients with symptomatic severe aortic stenosis after transcatheter aortic valve implantation (TAVI). We aimed to develop multimodal artificial intelligence (AI)-based models to predict long-term…
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Artikel Jurnal
Effect of a computer-aided quality feedback system on colonoscopists’ adenoma detection rate in Denmark: a multicentre, stepped-wedge, cluster-randomised, controlled trial
Summary: Background: Colonoscopy is a frequently performed procedure to diagnose and treat colorectal cancer. However, endoscopist competence varies considerably. Adenoma detection rate (ADR) is the most established indicator of colonoscopy quality. We aimed to evaluate the effect of a…
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Artikel Jurnal
Development of a target product profile for artificial intelligence in diabetic eye screening in England: a modified Delphi consensus study
Summary: Artificial intelligence (AI) health-care technologies offer a means of addressing the growing gap between health-care capacity and demand. However, few technologies have met the complex requirements of health-care systems for adoption. Diabetic eye screening (DES) in England exemplifies…
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Artikel Jurnal
Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography
Summary: Electroencephalography is the most commonly used diagnostic tool for epilepsy. However, interpreting electroencephalograms (EEGs) requires expertise that is not widely available. Advances in digital technology and wearables have enabled large-scale EEG recording, generating vast amounts of…
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Artikel Jurnal
Status, challenges, and future directions of machine learning in the management of epilepsy: a systematic review and meta-analysis
Summary: Background: Despite advances in epilepsy treatment options, selecting the appropriate therapy for an individual with epilepsy is a process of trial and error. Machine learning holds the potential to support clinical decision making. We aimed to provide an overview of the role of machine…
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Artikel Jurnal
Machine learning prediction for epilepsy treatment selection and prognosis: achievements and challenges
Summary: Accurate prediction of treatment response and selection of optimal treatments remain challenging in epilepsy management. With no reliable surrogate biomarkers for treatment response, the current process of selecting an antiseizure medication remains largely a trial-and-error approach…
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Artikel Jurnal
Toward unified and comprehensive automated electroencephalogram interpretation: a multicentre development and validation of an electroencephalogram foundation model
Summary: Background: Electroencephalogram (EEG) interpretation is essential for neurological diagnosis, but expert interpretation is limited globally, and existing AI methods address narrow tasks. This study aimed to develop and externally validate a broadly applicable foundation model capable of…
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Artikel Jurnal
Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee
Summary: Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with diabetes, because of early detection and treatment of sight-threatening disease. There is long-standing interest in the possibility of automating parts of this process through artificial…
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Artikel Jurnal
Efficacy of digital health technologies in the management of inflammatory bowel disease: an umbrella review
Summary: The use of digital health technology (DHT) is increasing worldwide. Clinical trials assessing available health tools for the management of patients with inflammatory bowel disease (IBD) are sparse, with limited evidence-based outcome data. In this umbrella review, we investigated the…
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Artikel Jurnal
AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study
Summary: Background: CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric…
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Artikel Jurnal
Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials?
Summary: Randomised controlled trials are the gold standard to assess the effectiveness and safety of clinical interventions; however, many paediatric trials are discontinued early due to challenges in patient enrolment. Hence, most paediatric clinical trials suffer from lack of adequate power…
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Artikel Jurnal
Utilising the Benefit Risk Assessment of Vaccines (BRAVE) toolkit to evaluate the benefits and risks of Vaxzevria in the EU: a population-based study
Summary: Background: Several COVID-19 vaccines have been licensed. To support the assessment of safety signals, we developed a toolkit to support COVID-19 vaccine monitoring and benefit–risk assessment. We aim to show the application of our toolkit in the EU using thrombosis with thrombocytopenia…
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Artikel Jurnal
Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study
Summary: Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence…
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Artikel Jurnal
Evaluation of mobile health technology combining telemonitoring and teleintervention versus usual care in vulnerable-phase heart failure management (HERMeS): a multicentre, randomised controlled trial
Summary: Background: The potential of mobile health (mHealth) technology combining telemonitoring and teleintervention as a non-invasive intervention to reduce the risk of cardiovascular events in patients with heart failure during the early post-discharge period (ie, the vulnerable phase) has not…
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Artikel Jurnal Teks lengkap
Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review
This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging…
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Artikel Jurnal Teks lengkap
Ethical and regulatory challenges of large language models in medicine
With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them…