The PERFORM Study: Artificial Intelligence Versus Human Residents in Cross-Sectional Obstetrics-Gynecology Scenarios Across Languages and Time Constraints [0.03%]
多语言和时间限制下的妇产科场景中的人工智能与住院医师的诊断能力比较研究（PERFORM研究）
Canio Martinelli,Antonio Giordano,Vincenzo Carnevale et al.
Canio Martinelli et al.
Objective: To systematically evaluate the performance of artificial intelligence (AI) large language models (LLMs) compared with obstetrics-gynecology residents in clinical decision-making, examining diagnostic accuracy a...
Global Artificial Intelligence Arms Race: The Future of Artificial Intelligence in Medicine [0.03%]
全球人工智能军备竞赛：医学领域的人工智能未来展望
Hamrish Kumar Rajakumar
Hamrish Kumar Rajakumar
Corrigendum to "Experience With an Optical Character Recognition Search Application for Review of Outside Medical Records" [0.03%]
“光学字符识别搜索应用程序在审阅外部医疗记录中的应用经验”的勘误表
[This corrects the article DOI: 10.1016/j.mcpdig.2024.08.001.]. © 2025 The Authors.
Optimizing Digital Management of Research and Collaboration With Academic Information Manager [0.03%]
利用学术信息经理进行科研和合作的数字管理及优化
Peyman Nejat,Vitali Fedosov,Chady Meroueh et al.
Peyman Nejat et al.
Objective: To evaluate the efficacy, efficiency, and usability of the current iteration of the fully automatic Academic Information Manager (AIM) within the Department of Anesthesiology and Perioperative Medicine. ...
Erratum to Leveraging the Metaverse for Enhanced Longevity as a Component of Health 4.0 [Mayo Clinic Proceedings: Digital Health. 2024;2:139-151] [0.03%]
关于《利用元宇宙增强健康4.0中的长寿成分[Mayo Clinic Proceedings: Digital Health，2024;2:139-151]》的勘误通知
[This corrects the article DOI: 10.1016/j.mcpdig.2024.01.007.]. © 2025 The Authors.
External Validation of Persistent Severe Acute Kidney Injury Prediction With Machine Learning Model [0.03%]
基于机器学习预测模型的持续性急性严重肾损伤的外部验证
Simone Zappalà,Francesca Alfieri,Andrea Ancona et al.
Simone Zappalà et al.
Objective: To externally validate the persistent electronic alert (PersEA) machine learning model for predicting persistent severe acute kidney injury (psAKI), addressing the scarcity of validated prediction models. ...
Comparison of Participant and Site Perceptions of Decentralized Clinical Trials in the USA [0.03%]
美国去中心化临床试验的参与者与站点观点比较研究
Roland Barge,Patrick Floody
Roland Barge
Objective: To define potential participant and site perceptions of decentralized clinical trials (DCTs). Participants and methods: Two ...
A Model for Rapid Innovation for Engagement, Enrollment, and Data and Sample Collection in a Diverse Cohort Study: Insights from All of Us Participant Labs [0.03%]
一套针对多样化队列研究的快速创新参与、注册和数据及样本收集模型：来自美国所有居民参与者实验室的研究体会
Janna Ter Meer,Jessica Chen,Romina Foster-Bonds et al.
Janna Ter Meer et al.
Objective: To improve engagement and retention of a cohort that reflects the US population within the All of Us Research Program, we created and implemented an innovation infrastructure and initiatives. ...
What Becomes of the Human Touch in the Age of Generative Artificial Intelligence? [0.03%]
生成式人工智能时代的人文关怀何去何从？
Kishwen Kanna Yoga Ratnam
Kishwen Kanna Yoga Ratnam
Much More Than the Malady: The Promise of a Web-Based Digital Platform Incorporating Self-Report for Research and Clinical Care in Mild Cognitive Impairment [0.03%]
记忆门诊数字化平台的建立及应用展望——以轻度认知障碍为例
Andrew McGarry,Oliver Roesler,Jackson Liscombe et al.
Andrew McGarry et al.
Traditional clinical trials in neurodegenerative disorders have utilized combinations of examination-based outcomes, global assessments by investigators and participants, and scales aimed at function, some of which are patient-reported outc...