Real-World Implementation of Large Language Models for Writing Clinical Discharge Summaries Within a Secure Data Environment: Development and Expert Evaluation Study [0.03%]
大型语言模型在安全数据环境下撰写临床出院总结的实施与专家评估研究
Catalina Carenzo,Kathleen Goldsmith,Maite Arribas et al.
Catalina Carenzo et al.
Background: A discharge summary should be a clinical report that documents a patient's hospital stay, including test results, diagnoses, management, and follow-up. Currently, discharge summaries are written by clinicians ...
Prediction of Type 2 Diabetes Mellitus From Chest X-Rays Using a Suite of Previously Developed Chronic Disease Deep Learning Models in an Ethnically Diverse Cohort: Observational Study [0.03%]
基于胸部X光片利用一组已经开发的老年慢性疾病深度学习模型预测二型糖尿病：观察性研究
Pola Lydia Lagari,Awais Farooq,Brian Thomas Layden et al.
Pola Lydia Lagari et al.
Background: Screening for type 2 diabetes (T2D) is not optimal, leading to a large number of patients being undiagnosed. Recently, deep learning (DL) applied to chest radiographs (CXRs) has shown promise for opportunistic...
Ambient AI Scribes and Emergency Department Documentation Burden: Retrospective Cohort Study [0.03%]
关于环境AI记录员和急诊科文档负担的回顾性队列研究
Carl Preiksaitis,Al&#x;ai Alvarez,Maia Winkel et al.
Carl Preiksaitis et al.
Background: Clinician burnout has reached crisis levels in emergency medicine, with clinical documentation burden identified as a central contributing factor. Ambient artificial intelligence (AI) scribes offer a promising...
Supporting Radiology Resident Education and Clinical Decision-Making With Large Language Models: Comparative Study of Reasoning Models DeepSeek-R1 and ChatGPT-o1 [0.03%]
大型语言模型在放射学住院医师教育和临床决策中的应用：DeepSeek-R1与ChatGPT-o1的比较研究
Semil Eminovic,Robin Schmidt,Bogdan Levita et al.
Semil Eminovic et al.
Background: Radiology trainees require efficient, accurate, and accessible resources to master complex imaging techniques and identify findings that guide clinical decision-making. Large language models (LLMs) are emergin...
Patient Perceptions on the Use of Artificial Intelligence in Creating Clinical Research Documents: Survey Study [0.03%]
关于使用人工智能创建临床研究文档的患者感知调查研究
Kimbra Edwards,Samuel Entwisle,Zack Fey et al.
Kimbra Edwards et al.
Background: The use of generative artificial intelligence (AI) by pharmaceutical companies and other organizations for preparing patient-facing documents reporting results of clinical research is becoming more common. Thi...
Application of Language Models for the Analysis of Adverse Drug Events in Pharmaceutical Research and Development: Scoping Review [0.03%]
语言模型在制药研究和开发中的药物不良事件分析的应用：雪球回顾分析研究
Oren Schreier,Anthony Yazdani,Ioannis Galdadas et al.
Oren Schreier et al.
Background: Adverse drug events (ADEs) remain a critical safety issue in pharmaceutical research and development (Pharma R&D), necessitating robust methods for early detection and surveillance. Language models (LMs) are i...
Review
JMIR AI. 2026 Jun 16:5:e77732. DOI:10.2196/77732 2026
Correction: Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study [0.03%]
修正：使用下颌裁剪颅面图像进行深度学习年龄估计和性别预测的比较模型开发与验证研究
Vitria Wuri Handayani,Mieke Sylvia Margaretha Amiatun Ruth,Riries Rulaningtyas et al.
Vitria Wuri Handayani et al.
AI-Assisted Systematic Literature Review of the Economic Burden of Pneumococcal Disease: Development and Validation Study [0.03%]
基于人工智能的肺炎球菌病经济负担系统文献回顾开发与验证研究
Dong Wang,Surabhi Datta,Julie Glasgow et al.
Dong Wang et al.
Background: Automated systematic literature review (SLR) may reduce the workload and errors associated with manual review, enabling faster, up-to-date reviews even with increasing publication volumes. Large language model...
Knowledge-Augmented Large Language Model for Multimodal Electronic Health Record-Based Risk Prediction: Development and Validation Study [0.03%]
基于多模态电子健康记录的风险预测的知识增强型大语言模型的研发与验证研究
Rituparna Datta,Jiaming Cui,Zihan Guan et al.
Rituparna Datta et al.
Background: Accurate clinical outcome prediction using electronic health records (EHRs) is crucial for patient care and resource allocation. EHRs include both structured data and rich, unstructured clinical notes. However...
A Structured Comparison of the Coalition for Health AI Responsible AI Guide and South Korea's Trustworthy AI Guideline for Health Care AI Assurance: Comparative Framework Analysis [0.03%]
健康人工智能责任指南与韩国医疗保健人工智能保障可信人工智能指南的结构化比较：比较框架分析
Trevor Vigeant,Aidan Tam,Qiming Shi et al.
Trevor Vigeant et al.
Background: Trustworthy artificial intelligence (AI) in health care requires assurance frameworks that translate ethical principles into measurable governance and evaluation practices. While a growing number of AI assuran...