Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics [0.03%]
使用个人基础模型的AI个性化可导致更精确的数字治疗学应用于心理健康领域
Peter Washington
Peter Washington
Digital health interventions often use machine learning (ML) models to make predictions of repeated adverse health events. For example, models may be used to analyze patient data to identify patterns that can anticipate the likelihood of di...
A Real-Time Signal-Based Wavelet Long Short-Term Memory Method for Length-of-Stay Prediction for the Intensive Care Unit: Development and Evaluation Study [0.03%]
基于实时信号的长时记忆小波神经网络用于重症监护病房住院时间预测：发展与评定研究
Yiqun Jiang,Qing Li,Wenli Zhang
Yiqun Jiang
Background: Efficient allocation of health care resources is essential for long-term hospital operation. Effective intensive care unit (ICU) management is essential for alleviating the financial strain on health care syst...
Deep Learning Multi-Modal Melanoma Detection: Algorithm Development and Validation [0.03%]
基于深度学习的多模态黑色素瘤检测算法的开发与验证
Nithika Vivek,Karthik Ramesh
Nithika Vivek
Background: The visual similarity of melanoma and seborrheic keratosis has made it difficult for older patients with disabilities to know when to seek medical attention, contributing to the metastasis of melanoma. ...
Mohammed As&#x;ad,Nawarh Faran,Hala Joharji
Mohammed As&#x;ad
Shared decision-making is central to patient-centered care but is often hampered by artificial intelligence (AI) systems that focus on technical transparency rather than delivering context-rich, clinically meaningful reasoning. Although AI ...
Assessing Revisit Risk in Emergency Department Patients: Machine Learning Approach [0.03%]
急诊患者的再访风险评估：机器学习方法
Wang-Chuan Juang,Zheng-Xun Cai,Chia-Mei Chen et al.
Wang-Chuan Juang et al.
Background: Overcrowded emergency rooms might degrade the quality of care and overload the clinic staff. Assessing unscheduled return visits (URVs) to the emergency department (ED) is a quality assurance procedure to iden...
Training Language Models for Estimating Priority Levels in Ultrasound Examination Waitlists: Algorithm Development and Validation [0.03%]
用于估算超声波检查候诊名单优先级别的语言模型训练：算法开发与验证
Kanato Masayoshi,Masahiro Hashimoto,Naoki Toda et al.
Kanato Masayoshi et al.
Background: Ultrasound examinations, while valuable, are time-consuming and often limited in availability. Consequently, many hospitals implement reservation systems; however, these systems typically lack prioritization f...
Natural Language Processing for Identification of Hospitalized People Who Use Drugs: Cohort Study [0.03%]
用于识别住院药物使用者的自然语言处理研究：队列研究
Taisuke Sato,Emily D Grussing,Ruchi Patel et al.
Taisuke Sato et al.
Background: People who use drugs (PWUD) are at heightened risk of severe injection-related infections. Current research relies on billing codes to identify PWUD-a methodology with suboptimal accuracy that may underestimat...
AI-SDM: A Concept of Integrating AI Reasoning into Shared Decision-Making [0.03%]
AI-SDM：在共同决策中整合人工智能推理的概念
Mohammed As&#x;ad,Nawarh Faran,Hala Joharji
Mohammed As&#x;ad
Shared decision-making is central to patient-centered care but is often hampered by AI systems that focus on technical transparency rather than delivering context-rich, clinically meaningful reasoning. Although XAI methods elucidate how dec...
Deep Learning Multi Modal Melanoma Detection: Algorithm Development and Validation [0.03%]
基于深度学习的多模态黑色素瘤检测算法研发与验证
Nithika Vivek,Karthik Ramesh
Nithika Vivek
Background: The visual similarity of melanoma and seborrheic keratosis has made it difficult for elderly patients with disabilities to know when to seek medical attention, contributing to the metastasis of melanoma. ...
Enhancing Magnetic Resonance Imaging (MRI) Report Comprehension in Spinal Trauma: Readability Analysis of AI-Generated Explanations for Thoracolumbar Fractures [0.03%]
人工智能生成的胸腰椎骨折磁共振成像（MRI）报告解释的可读性分析增强脊柱创伤 MRI 报告的理解：胸腰椎骨折 MRI 报告人工智能解释的可读性分析
David C Sing,Kishan S Shah,Michael Pompliano et al.
David C Sing et al.
Background: Magnetic resonance imaging (MRI) reports are challenging for patients to interpret and may subject patients to unnecessary anxiety. The advent of advanced artificial intelligence (AI) large language models (LL...