Accelerating AI in clinical care: policy priorities of the 2025 HHS request for information [0.03%]
加速人工智能在临床护理中的应用：2025年HHS信息征询的政策优先事项
David C Klonoff,Agatha F Scheideman,Mandy M Shao et al.
David C Klonoff et al.
A U.S. Department of Health and Human Services Request for Information seeks input on “Accelerating the Adoption and Use of Artificial Intelligence as Part of Clinical Care.” This solicitation could impact future policies on regulation, r...
Fully-automated sleep staging for Parkinson's disease and isolated REM sleep behavior disorder [0.03%]
帕金森病和孤立性快速眼动睡眠行为障碍的全自动睡眠分期分类算法研究
Jesper Strøm,Casper Skjærbæk,Natasha Becker Bertelsen et al.
Jesper Strøm et al.
Isolated REM sleep behavior disorder (iRBD) is a key prodromal marker of Parkinson's disease (PD). Video-polysomnography (vPSG) remains the diagnostic gold standard, but manual sleep staging is particularly time-consuming and challenging in...
Elizabeth C Stade,Zoe M Tait,Samuel T Campione et al.
Elizabeth C Stade et al.
The extent to which people use general-purpose large language models (LLMs) for their mental health is unknown. Information about use patterns is important for clinicians, developers, and regulators. We surveyed U.S. adults (n = 1871) betwe...
Reply to: Surgical scene understanding and the emerging challenge of independent validation in an industry-led AI ecosystem [0.03%]
述评：外科手术场景理解及以行业为主导的AI生态系统中独立验证所面临的挑战
Matthias Carstens,Shubha Vasisht,Zheyuan Zhang et al.
Matthias Carstens et al.
In this reply to MacAonghusa and Cahill, we reflect on fundamental validation challenges in AI-enabled surgical scene understanding and propose key priorities that academic, clinical, and industry stakeholders must collaboratively address t...
Surgical scene understanding and the structural validation gap in an industry-led AI ecosystem [0.03%]
外科手术场景理解及以行业为主导的人工智能生态系统中的结构验证差距
Pol MacAonghusa,Ronan A Cahill
Pol MacAonghusa
This Matters Arising builds upon the review by Carstens et al. highlighting methodological limitations within the academic surgical scene understanding (SSU) literature. Increasingly clinically deployed SSU occurs within commercial platform...
Author Correction: Clinically-guided models or foundation models? predicting cervical spondylotic myelopathy from electronic health records [0.03%]
作者更正：临床指导模型还是基础模型？从电子健康记录预测颈椎病性脊髓病
Salim Yakdan,Ben Warner,Zoher Ghogawala et al.
Salim Yakdan et al.
Published Erratum
NPJ digital medicine. 2026 Aug 8;9(1):610. DOI:10.1038/s41746-026-03043-0 2026
Promoting health equity through linguistic justice: mitigating embedded bias of large language models [0.03%]
通过语言正义促进健康公平：缓解大型语言模型的嵌入偏见
Weipeng Han,Sol Richardson,Jiming Zhu et al.
Weipeng Han et al.
The application of Large Language Models (LLMs) in healthcare prompts critical reflection on the impact of linguistic justice on health equity. Disparities in data availability across languages during the training and evaluation phases of L...
Developing interactive learning interfaces for teaching AI in health professions education using generative AI tools [0.03%]
利用生成式人工智能工具开发交互式学习界面以在健康专业教育中教授人工智能技能培训
Peter Washington,Anoop Muniyappa,Chen Liang et al.
Peter Washington et al.
Health professions education (HPE) must now equip students to use artificial intelligence (AI) tools safely. We offer an instructional design strategy for HPE educators aiming to teach fundamental AI concepts to non-technical audiences: usi...
Prehospital Injury Severity Estimate (PHISE) matches in-hospital trauma scores when embedded in AI models [0.03%]
嵌入人工智能模型中的院前损伤严重程度评估（PHISE）与院内创伤评分相匹配
Manuel Sigle,Andreas Goldschmied,Meinrad Gawaz et al.
Manuel Sigle et al.
Trauma severity scores such as the Injury Severity Score (ISS) and New Injury Severity Score (NISS) are widely used for trauma benchmarking, risk stratification and outcome prediction, but rely on diagnostic imaging unavailable in the preho...
Interpreting the clinical utility and generalizability of a multitask perioperative prediction model [0.03%]
多任务麻醉预测模型的临床效用和普适性的解读
Xiao-Min Wo,Guo-Ming Zhang
Xiao-Min Wo
This Matters Arising comments on Yoon et al.'s multitask gradient boosting model for predicting acute kidney injury, postoperative respiratory failure and in-hospital mortality after non-cardiac surgery. We ask for clarification on the clin...