Qianyi Xu,Feng Wu,Zi Yi Christopher Thong et al.
Qianyi Xu et al.
Renal replacement therapy (RRT) is a critical intervention for patients with acute kidney injury (AKI). However, clinical decision-making regarding the timing of initiation, modality selection, optimal ultrafiltration rate, and weaning crit...
Voice-controlled super-resolution ultrasound imaging and reporting powered by multimodal large language models [0.03%]
Ning Guo,Zixuan Deng,Qin Tan et al.
Ning Guo et al.
Super-resolution ultrasound imaging (SRUI) surpasses the diffraction limit of conventional ultrasound, enabling visualization of microvascular architecture and hemodynamics with potential applications in neurology, oncology, and cardiology....
Interpretable machine learning model for predicting kidney failure among CAKUT children in multicenter large-scale study [0.03%]
基于多中心大规模研究的CAKUT儿童肾衰竭预测模型及其可解释性分析
Tianyi Liu,Helin Wang,Jialu Liu et al.
Tianyi Liu et al.
Congenital anomalies of the kidney and urinary tract (CAKUT) are the leading cause of pediatric kidney failure, but predicting individual progression remains challenging. This multicenter study developed and validated POCC, a machine learni...
Joshua Strong,Harry Rogers,Emma Sun et al.
Joshua Strong et al.
Artificial intelligence is increasingly embedded in clinical pathways, making effective human-AI collaboration (HAIC) a practical and policy priority in healthcare. We conducted a scoping review of empirical studies of HAIC in healthcare pu...
Treatment-aware deep learning enables counterfactual prediction of individual benefit from PARP inhibitors in ovarian cancer [0.03%]
治疗意识深度学习能够对卵巢癌患者从PARP抑制剂中获益情况进行反事实预测
Jean-Sébastien Frenel,Pierre-Etienne Heudel,Emmanuelle Guinaudeau et al.
Jean-Sébastien Frenel et al.
Homologous recombination deficiency (HRD) assays are used to select patients with ovarian cancer for PARP inhibitors, but they do not fully capture the heterogeneity of treatment benefit. Using data from the phase III PAOLA-1 randomized tri...
Quantifying effects of the European Health Data Space on the app ecosystem and data access [0.03%]
欧洲健康数据空间对应用程序生态系统和数据访问影响的量化分析报告
Stefanie Brückner,Ronja Riedel,Sven Hetmank et al.
Stefanie Brückner et al.
Access to patient-generated health data from mobile apps and wearables is increasingly central to connected care. The European Health Data Space (EHDS) introduces app providers as health data holders with obligations to share data for patie...
Yan Zhuang,Bo Wang,Chengliang Yin et al.
Yan Zhuang et al.
In the quest to enhance medical consultation, our study introduces AI4Doctor, a sophisticated large-language model (LLM) tailored for the clinical domain. At the heart of AI4Doctor is an innovative integration strategy that synergizes disti...
A scoping review of human-AI collaboration patterns and task divisions in healthcare applications [0.03%]
医疗应用中的人工智能协作模式和任务分工的综述性研究
Yuxuan You,Xi Li
Yuxuan You
With the widespread use of Artificial Intelligence (AI) in healthcare, how human physicians work with AI, a new colleague, has become an issue of increasing concern. This review systematically explores the collaboration between humans and A...
Young people's perceptions and recommendations for conversational generative artificial intelligence in youth mental health [0.03%]
年轻人对青年心理健康中对话式生成人工智能的感知和建议
Adam Poulsen,Ian B Hickie,Carla Gorban et al.
Adam Poulsen et al.
Conversational generative artificial intelligence agents (or genAI chatbots) could benefit youth mental health, yet young people's perspectives remain underexplored. We examined the Mental health Intelligence Agent (Mia), a genAI chatbot or...
Segmenting with confidence through uncertainty quantification for brain tumor imaging [0.03%]
基于不确定性量化并通过自信度划分脑肿瘤成像的方法
Yassine Guennoun,Pierre Nedelec,Mark McArthur et al.
Yassine Guennoun et al.
A major barrier to clinical adoption of artificial intelligence (AI) for brain tumor monitoring is the lack of calibrated uncertainty in automated segmentation, limiting clinician trust. We developed a deep learning framework that generates...