Mayo Clinic Proceedings. Digital health最新文献

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A Scoping Review of Large Language Models in Personal Sleep Wellness 个人睡眠健康的大语言模型的范围综述

Mayo Clinic Proceedings. Digital health

Pub Date : 2025-12-01 DOI: 10.1016/j.mcpdig.2025.100301

Hamid Mansoor PhD

As sleep health becomes increasingly central to personal well-being, individuals are turning to digital tools for education, tracking, and behavior change support. Large language models like ChatGPT and Gemini have recently emerged as promising components of these tools, capable of generating personalized, conversational, and context-aware sleep guidance. This scoping survey synthesizes findings from 21 papers that explore the use of large language models in nonclinical, everyday user-focused applications for sleep health. We organize the literature into 4 core use cases: educational question answering, condition-specific support (eg, obstructive sleep apnea), personalized recommendations and coaching, and cognitive behavioral therapy–based self-help systems. We analyze the diverse data sources involved—including wearable sensor data, self-reported metrics, and synthetic benchmarks—as well as model architectures, fine-tuning techniques, and personalization strategies. Finally, we examine evaluation frameworks ranging from expert review to pilot user studies and LLM-based scoring. The review highlights current capabilities, methodological challenges, and open opportunities for advancing trustworthy, personalized sleep support using generative artificial intelligence, while emphasizing that much of the evidence remains preliminary, often short-term, expert-rated, or proxy-based, which limits external validity and generalizability.

随着睡眠健康越来越成为个人幸福的核心，人们开始转向数字工具来进行教育、跟踪和行为改变支持。像ChatGPT和Gemini这样的大型语言模型最近作为这些工具的有前途的组成部分出现，能够生成个性化的、会话的和上下文感知的睡眠指导。这项范围调查综合了21篇论文的发现，这些论文探索了在非临床、以用户为中心的日常睡眠健康应用中使用大型语言模型。我们将文献组织为4个核心用例：教育问答，特定条件支持（例如，阻塞性睡眠呼吸暂停），个性化建议和指导，以及基于认知行为治疗的自助系统。我们分析了所涉及的各种数据源，包括可穿戴传感器数据、自我报告的指标和综合基准，以及模型架构、微调技术和个性化策略。最后，我们研究了评估框架，从专家审查到试点用户研究和基于法学硕士的评分。该综述强调了目前的能力、方法上的挑战，以及利用生成式人工智能推进可信赖的个性化睡眠支持的开放机会，同时强调许多证据仍然是初步的，通常是短期的、专家评估的或基于代理的，这限制了外部有效性和可泛化性。

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引用次数: 0

Inline Electronic Medical Record Workplace Violence (WPV) Documentation and Utilization of Automation Increases Capture of WPV Reporting Amongst Emergency Department Staff Within a Large Health System 在线电子医疗记录工作场所暴力（WPV）文档和自动化的使用增加了大型卫生系统内急诊科工作人员对WPV报告的捕获

Mayo Clinic Proceedings. Digital health

Pub Date : 2025-12-01 DOI: 10.1016/j.mcpdig.2025.100288

Sarayna S. McGuire MD MS , Matthew R. Neville MS , Michael Elligan II MBA, PMP , Heather A. Heaton MD , Kristine M. Thompson MD , James E. Colletti MD , Abi Jireh G. Paraon MSN RN NI-BC , David J. Julson , Aidan F. Mullan MA , Casey M. Clements MD PhD

引用次数: 0

Optimizing LLM Performance for Radiology Report Mining Through Prompt Engineering 通过提示工程优化LLM在放射学报告挖掘中的性能

Mayo Clinic Proceedings. Digital health

Pub Date : 2025-12-01 DOI: 10.1016/j.mcpdig.2025.100290

Mrinal K. Dhar PhD , Muhammed Khalifa MD , Elif G. Bozkurt MD , Samuel C. Buchl BA , Adriana V. Gregory PhD , Timothy L. Kline PhD

引用次数: 0

Predict and Prepare: AI-Driven Insights for Clinical Resource Planning 预测和准备：人工智能驱动的临床资源规划见解

Mayo Clinic Proceedings. Digital health

Pub Date : 2025-12-01 DOI: 10.1016/j.mcpdig.2025.100286

Lindsey Asher DHA, Greg Davis MHA, FACHE, Brendan M. Carr MD, James E. Colletti MD, Laura E. Walker MD, Derick D. Jones MD

引用次数: 0

Graph-Based Deep Ensemble Learning to Enhance Diagnostic Efficiency in Lung Adenocarcinoma H&E-Stained Histopathological Subtyping 基于图的深度集成学习提高肺腺癌h&e染色病理分型的诊断效率

Mayo Clinic Proceedings. Digital health

Pub Date : 2025-12-01 DOI: 10.1016/j.mcpdig.2025.100285

Mohammad Mehdi Hosseini , Meghdad Sabouri Rad , Junze (Vincent) Huang , Rakesh Choudhary , Saverio J. Carello , Ola El-Zammar , Michel Nasr , Bardia Rodd

引用次数: 0

Integrating U-Net in a LLM Supervisor Agent Pipeline for Pancreatic Ductal Adenocarcinoma Diagnosis 整合U-Net在LLM监督代理管道中诊断胰腺导管腺癌

Mayo Clinic Proceedings. Digital health

Pub Date : 2025-12-01 DOI: 10.1016/j.mcpdig.2025.100287

Rahul Gomes PhD, William L. Jerome BS, Sushil K. Garg MBBS

引用次数: 0

The AENEAS Project: Intraoperative Anatomical Guidance Through Real-Time Landmark Detection Using Machine Vision AENEAS项目：使用机器视觉通过实时地标检测进行术中解剖指导

Mayo Clinic Proceedings. Digital health

Pub Date : 2025-12-01 DOI: 10.1016/j.mcpdig.2025.100308

Simone Olei MD , Gary Sarwin MSc , Victor E. Staartjes MD, PhD , Luca Zanuttini MD , Seungjun Ryu MD , Luca Regli MD , Ender Konukoglu PhD , Carlo Serra MD

Objective

To investigate the performance of a deep learning machine vision-based model in identifying anatomical landmarks in a complex microsurgical setting, such as the pterional trans-Sylvian approach.

Patients and Methods

We developed a deep learning object detection model (YOLOv7x) trained on 5307 labeled frames from 78 surgical videos of 76 patients undergoing pterional trans-Sylvian approach from January 1, 2020 to June 31, 2024. Surgical steps were standardized, and key anatomical targets—frontal/temporal dura, inferior frontal/superior temporal gyri, optic and olfactory nerves, and internal carotid artery—were annotated by specifically trained neurosurgical residents and verified by the operating surgeon. Bounding boxes derived from segmentation masks served as training inputs. Performance was evaluated using 5-fold cross-validation.

Results

The model achieved promising detection performance for deep structures, particularly the optic nerve (average precision at an intersection over union threshold of 0.50 [AP₅₀]: 0.73) and internal carotid artery (AP₅₀: 0.67). Superficial structures, like dura and cortical gyri, had lower precision (AP₅₀ range: 0.25-0.45), likely due to morphological similarity and optical variability. Performance variability across classes reflects the complexity of the anatomical setting along with data limitations.

Conclusion

Applying machine vision techniques for anatomical detection in a complex neurosurgical setting is feasible. Although challenges remain in detecting less distinctive structures, the high accuracy achieved for deep anatomical landmarks validates this approach. This study marks an essential step toward the development of machine vision-powered anatomical recognition tools, with the prospective goal of improving intraoperative orientation and reducing variability among surgeons.

目的探讨基于深度学习机器视觉的模型在复杂显微外科手术环境（如翼点跨sylvian入路）中解剖标志识别的性能。患者和方法我们开发了一个深度学习目标检测模型（YOLOv7x），该模型对76名患者在2020年1月1日至2024年6月31日期间接受翼点跨sylvian入路手术的78个手术视频中的5307个标记帧进行了训练。手术步骤标准化，关键解剖目标额/颞硬脑膜、额下回/颞上回、视神经和嗅觉神经、颈内动脉由专门训练的神经外科住院医师注释并由手术医生验证。分割蒙版生成的边界框作为训练输入。使用5倍交叉验证评估性能。结果该模型对深层结构的检测效果很好，特别是视神经（交叉点的平均精度为0.50 [AP50]: 0.73）和颈内动脉（AP50: 0.67）。表面结构，如硬脑膜和皮质脑回，精度较低（AP50范围：0.25-0.45），可能是由于形态相似性和光学变异性。不同类别的表现差异反映了解剖环境的复杂性以及数据的局限性。结论应用机器视觉技术进行复杂神经外科解剖检测是可行的。尽管在检测不太明显的结构方面仍然存在挑战，但深层解剖标志的高精度验证了这种方法。这项研究标志着机器视觉驱动的解剖识别工具的发展迈出了重要的一步，其预期目标是改善术中定位和减少外科医生之间的差异。

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引用次数: 0

Foundation Models and Their Applications in Gastrointestinal Endoscopy 基础模型及其在胃肠内镜检查中的应用

Mayo Clinic Proceedings. Digital health

Pub Date : 2025-12-01 DOI: 10.1016/j.mcpdig.2025.100282

Jeffrey R. Fetzer PhD , Saghir A. Al-Fasly PhD , Cadman L. Leggett MD , Nayantara Coelho-Prabhu MD , Shounak Majumder MD , John B. League III MMIS , Shradha Shalini MS , Ghazal Alabtah , Christine V. Dvorak MAOL , Hamid R. Tizhoosh PhD

引用次数: 0

Feature Selection and Machine Learning Strategies Optimize a Minimal Molecular Assay for Cholangiocarcinoma Subtype 特征选择和机器学习策略优化了胆管癌亚型的最小分子检测

Mayo Clinic Proceedings. Digital health

Pub Date : 2025-12-01 DOI: 10.1016/j.mcpdig.2025.100283

Ellen L. Larson MD , Erik Jessen PhD , Dong-Gi Mun PhD , Jennifer L. Tomlinson MD , Amro M. Abdelrahman MBBS, MS , Danielle M. Carlson , Hojjat Salehinejad PhD , Rory L. Smoot MD

引用次数: 0

Electronic Health Records and Their Role in the Surveillance of Infectious Disease 电子健康记录及其在传染病监测中的作用

Mayo Clinic Proceedings. Digital health

Pub Date : 2025-11-29 DOI: 10.1016/j.mcpdig.2025.100307

Dimple Yasvin Chudasama PhD, Theresa Lamagni PhD, Colin Brown MRCP, Russell Hope PhD

引用次数: 0

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