Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach [0.03%]
基于深度学习集成的可解释心电图心律不齐检测：一种遗传程序设计方法
Arkadiusz Czerwinski,Damian Kucharski,Agata M Wijata et al.
Arkadiusz Czerwinski et al.
Cardiovascular diseases remain the leading cause of death in developed countries. This study introduces deep learning ensembles for arrhythmia detection and atrial fibrillation (AF) recurrence prediction from electrocardiogram scans, suppor...
A Bayesian-driven approach to identify racial inequities in longitudinal care delivery [0.03%]
一种识别纵向护理递送中种族不平等的Bayesian驱动方法
Aidan M Campbell,A James O&#x;Malley,Inas S Khayal
Aidan M Campbell
Measures of healthcare quality and equity often overlook when care is delivered, potentially masking important disparities. We present a novel, time-aware approach to detect racial inequities in hospice use among 100,480 Medicare beneficiar...
The perils of politeness: how large language models may amplify medical misinformation [0.03%]
谨小慎微的危险：大型语言模型可能会放大医学错误信息的风险
Kyra L Rosen,Margaret Sui,Kimia Heydari et al.
Kyra L Rosen et al.
Chen et al. demonstrate that large language models (LLMs) frequently prioritize agreement over accuracy when responding to illogical medical prompts, a behavior known as sycophancy. By reinforcing user assumptions, this tendency may amplify...
Improving dataset transparency in dermatologic Artificial Intelligence using a dataset nutrition label [0.03%]
利用数据集营养标签改善皮肤病人工智能数据集的透明度
Yingjoy Li,Matthew Taylor,Kasia S Chmielinski et al.
Yingjoy Li et al.
Biased and poorly documented dermatology datasets pose risks to the development of safe and generalizable artificial intelligence (AI) tools. We created a Dataset Nutrition Label (DNL) for multiple dermatology datasets to support transparen...
Emma Croxford,Yanjun Gao,Elliot First et al.
Emma Croxford et al.
Electronic Health Records (EHRs) contain vast clinical data that are difficult for providers to synthesize. Generative AI with Large Language Models (LLMs) can summarize records to reduce cognitive burden, but ensuring accuracy requires rel...
A generative AI teaching assistant for personalized learning in medical education [0.03%]
用于医学教育的生成式AI教学助手以实现个性化学习
Thomas Thesen,Soo Hwan Park
Thomas Thesen
Medical education faces a scalability crisis, where rising class sizes strain individualized instruction, while students increasingly adopt unvalidated Generative AI (GenAI) tools for individualized learning support. This study investigated...
Ben Li
Ben Li
The npj Digital Medicine Editorial Fellowship (https://www.nature.com/npjdigitalmed/editorial-fellowship) is a year-long program that provides trainees and early career researchers with direct exposure to peer review, editorial writing, and...
Can human connection amplify digital health outcomes? Familial involvement in a mobile health app [0.03%]
人类互动能否增强数字健康效果？家庭成员在移动健康应用程序中的参与度
Elizabeth J Enichen,Kimia Heydari,Ben Li et al.
Elizabeth J Enichen et al.
In “A Randomized Controlled Trial of Mobile Intervention Using Health Support Bubbles to Prevent Social Frailty”, Hayashi et al. investigated the effects of using a mobile health app with family or individually. Greater improvements in so...
Felix Richter,Emma Holmes,Florian Richter et al.
Felix Richter et al.
AI is transforming healthcare, yet pediatric adoption remains limited and governance is underdeveloped. We review existing frameworks and identify pediatric-specific gaps: insufficient stakeholder engagement, developmentally appropriate con...
Electrophysiological signatures predict the therapeutic window of deep brain stimulation electrode contacts [0.03%]
电生理信号可预测深部脑刺激电极触点的治疗窗口期
Fayed Rassoulou,Abhinav Sharma,Alexandra Steina et al.
Fayed Rassoulou et al.
Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease. Identifying the optimal parameters is a complex task. Here, we investigated whether electrophysiology, combined with machine learning, can support contact selec...