Building robust, proportionate, and timely approaches to regulation and evaluation of digital mental health technologies [0.03%]
构建稳健、适度和及时的数字心理健康技术监管和评估方法
Gareth Hopkin,Richard Branson,Paul Campbell et al.
Gareth Hopkin et al.
Demand for mental health services exceeds available resources globally, and access to diagnosis and evidence-based treatment is affected by long delays. Digital mental health technologies present an opportunity to reimagine the delivery of ...
Innovative diagnostic technologies: navigating regulatory frameworks through advances, challenges, and future prospects [0.03%]
创新诊断技术：通过进展、挑战和未来前景解读监管框架
Jesus Rodriguez-Manzano,Sumithra Subramaniam,Chibuzor Uchea et al.
Jesus Rodriguez-Manzano et al.
Diagnostic tools are key to guiding patient management and informing public health policies to control infectious diseases. However, many diseases still do not have effective diagnostics and much of the global population faces restricted ac...
Advancing the management of maternal, fetal, and neonatal infection through harnessing digital health innovations [0.03%]
利用数字健康创新推进母胎新生儿感染的防治
Damien K Ming,Abi Merriel,David M E Freeman et al.
Damien K Ming et al.
Infections occurring in the mother and neonate exert a substantial health burden worldwide. Optimising infection management is crucial for improving individual outcomes and reducing the incidence of antimicrobial resistance. Digital health ...
Using digital health technologies to optimise antimicrobial use globally [0.03%]
利用数字健康技术在全球范围内优化抗菌药物使用
Timothy M Rawson,Nina Zhu,Ronald Galiwango et al.
Timothy M Rawson et al.
Digital health technology (DHT) describes tools and devices that generate or process health data. The application of DHTs could improve the diagnosis, treatment, and surveillance of bacterial infection and the prevention of antimicrobial re...
The Lancet Digital Health
The Lancet Digital Health
Using fine-tuned large language models to parse clinical notes in musculoskeletal pain disorders [0.03%]
利用微调的大语料模型解析肌骨疼痛障碍的临床记录
Akhil Vaid,Isotta Landi,Girish Nadkarni et al.
Akhil Vaid et al.
Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review [0.03%]
基于医疗人工智能开发的公开COVID-19数据集透明度差距系统评价
Joseph E Alderman,Maria Charalambides,Gagandeep Sachdeva et al.
Joseph E Alderman et al.
During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-care resource constraints. Previous research shows that health-care datasets often have limitations, leading to biased AI technologies. This sy...
Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge [0.03%]
深度学习辅助泛癌腹部器官量化FLARE22挑战赛：释放未标注数据的力量
Jun Ma,Yao Zhang,Song Gu et al.
Jun Ma et al.
Deep learning has shown great potential to automate abdominal organ segmentation and quantification. However, most existing algorithms rely on expert annotations and do not have comprehensive evaluations in real-world multinational settings...
Strategies for integrating artificial intelligence into mammography screening programmes: a retrospective simulation analysis [0.03%]
人工智能在乳腺癌筛查项目中应用的策略仿真研究
Zacharias V Fisches,Michael Ball,Trasias Mukama et al.
Zacharias V Fisches et al.
Background: Integrating artificial intelligence (AI) into mammography screening can support radiologists and improve programme metrics, yet the potential of different strategies for integrating the technology remains unde...
Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study [0.03%]
基于人工智能的心电图用于死亡率和心血管风险评估的模型构建及验证研究
Arunashis Sau,Libor Pastika,Ewa Sieliwonczyk et al.
Arunashis Sau et al.
Background: Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not...