Can co-designed educational interventions help consumers think critically about asking ChatGPT health questions? Results from a randomised-controlled trial [0.03%]
共设计的教育干预措施能否帮助消费者批判性地思考向ChatGPT提出健康问题？随机对照试验结果
Julie Ayre,Melody Taba,Brooke Nickel et al.
Julie Ayre et al.
This randomised controlled trial evaluated two brief co-designed health literacy educational interventions (animation; images) to help people critically reflect on asking ChatGPT health questions. Australian adults with experience of ChatGP...
Ensemble learning approaches for early prediction of chronic kidney disease based on polysomnographic phenotype analysis [0.03%]
基于多导睡眠图表型分析的慢性肾脏病早期预测集成学习方法
Dong Hui Shin,Doljinsuren Enkhbayar,So Yeon Park et al.
Dong Hui Shin et al.
This study presents an ensemble learning approach for automated screening and severity classification of chronic kidney disease (CKD) using polysomnographic (PSG) phenotypes. We analyzed PSG data from 358 subjects (179 CKD, 179 early-CKD) i...
EVA-X: a foundation model for general chest x-ray analysis with self-supervised learning [0.03%]
基于自监督学习的通用胸部X光图像分析基础模型EVA-X
Jingfeng Yao,Xinggang Wang,Yuehao Song et al.
Jingfeng Yao et al.
Artificial intelligence analysis methods for chest X-ray images are limited by insufficient annotation data and varying levels of annotation, resulting in weak generalization ability and difficulty in clinical dissemination. Here, we presen...
A large language model-based approach to quantifying the effects of social determinants in liver transplant decisions [0.03%]
基于大型语言模型的方法量化社会决定因素在肝移植决策中的影响
Emily Robitschek,Asal Bastani,Kathryn Horwath et al.
Emily Robitschek et al.
Psychosocial risk factors and social determinants of health (SDOH) contribute to persistent disparities in liver transplantation access. We developed a large language model framework to extract and analyze how these factors influence care t...
Evaluating transparency in AI/ML model characteristics for FDA-reviewed medical devices [0.03%]
美国食品药物管理局审批的医疗设备中的人工智能/机器学习模型透明度评估
Viraj Mehta,Abhinav Komanduri,Rishabh Singh Bhadouriya et al.
Viraj Mehta et al.
The rapid integration of artificial intelligence (AI) and machine learning (ML) into medical devices has underscored the need for transparency in regulatory reporting. In 2021, the U.S. Food and Drug Administration (FDA) issued Good Machine...
Advancing the frontier of rare disease modeling: a critical appraisal of in silico technologies [0.03%]
推进罕见病建模的前沿：对计算技术的批判性评估
Francesca Pistollato,Fabia Furtmann,Lindsay J Marshall et al.
Francesca Pistollato et al.
Rare diseases affect over 300 million people worldwide and pose unique research challenges. In silico approaches, such as mechanistic models, machine learning, and simulations, offer scalable tools for disease characterisation, drug discove...
Promoting xenomorphic patient-facing AIs: The case against anthropomorphism in medical AIs [0.03%]
促进类生化患者互动AI的发展：医学人工智能中反人类化的论点
Stephen R Milford,Emma Herger,Johanna Eichinger et al.
Stephen R Milford et al.
The rapid emergence of patient-facing medical artificial intelligence (MAI) raises pressing questions about its design and impact on healthcare. Current anthropomorphic design strategies, which endow AIs with human-like features, are based ...
Evaluating the diagnostic accuracy of vision language models for neuroradiological image interpretation [0.03%]
评估视觉语言模型在神经放射学图像解释中的诊断准确性
Aymen Meddeb,Ida Rangus,Paolo Pagano et al.
Aymen Meddeb et al.
This study evaluates the diagnostic performance of commercial and open-source Vision-Language Models (VLMs) in neuroradiological image interpretation, using a dataset of 100 brain and spine cases from Radiopaedia. Five VLMs (Gemini 2.0, Ope...
Therapeutic outcomes of different treatment approaches for T1a-M3/T1b esophageal cancer and development of a prognostic prediction model [0.03%]
T1a-M3/T1b期食管癌不同治疗模式的疗效分析及预后预测模型构建
Aijing Zhu,Shu Huang,Shuaijing Huang et al.
Aijing Zhu et al.
The optimal treatment for esophageal cancer (EC) invading the muscularis mucosa (T1a-M3) or submucosa (T1b) remains debated. This study analyzed patients with stage T1a-M3/T1b EC from the SEER database (cycle 2004-2017). Cancer-specific sur...
Timelapse-based 3D reconstruction of blastocysts reveals 3D morphologies of human blastocysts [0.03%]
基于时间 lapse 的胚胎三维重建揭示了人类囊胚的三维形态学
Bo Huang,Keyi Si,Yaxian Guo et al.
Bo Huang et al.
Assessing blastocysts from a three-dimensional (3D) perspective introduces a novel approach to clinical embryo evaluation. Currently, assisted reproduction laboratories lack clinically compatible methods to reconstruct 3D blastocyst structu...