Aiding Large Language Models Using Clinical Scoresheets for Neurobehavioral Diagnostic Classification From Text: Algorithm Development and Validation [0.03%]
基于文本的神经行为诊断分类的临床评分表助力大型语言模型：算法开发与验证
Kaiying Lin,Abdur Rasool,Saimourya Surabhi et al.
Kaiying Lin et al.
Background: Large language models (LLMs) have demonstrated the ability to perform complex tasks traditionally requiring human intelligence. However, their use in automated diagnostics for psychiatry and behavioral science...
Comparison of Japanese Mpox (Monkeypox) Health Education Materials and Texts Created by Artificial Intelligence: Cross-Sectional Quantitative Content Analysis Study [0.03%]
日本猴痘（猴痘）健康教育材料与人工智能创建的文本比较：横断面定量内容分析研究
Shinya Ito,Emi Furukawa,Tsuyoshi Okuhara et al.
Shinya Ito et al.
Background: Mpox (monkeypox) outbreaks since 2022 have emphasized the importance of accessible health education materials. However, many Japanese online resources on mpox are difficult to understand, creating barriers for...
Use of Automated Machine Learning to Detect Undiagnosed Diabetes in US Adults: Development and Validation Study [0.03%]
美国成年人中使用自动机器学习检测未诊断糖尿病：发展与验证研究
Jianxiu Liu,Fred Ssewamala,Ruopeng An et al.
Jianxiu Liu et al.
Background: Early diagnosis of diabetes is essential for early interventions to slow the progression of dysglycemia and its comorbidities. However, among individuals with diabetes, about 23% were unaware of their conditio...
Deep Learning Models to Screen Electronic Health Records for Breast and Colorectal Cancer Progression: Performance Evaluation Study [0.03%]
基于深度学习的电子健康档案筛选乳腺癌和结直肠癌进展模型性能评估研究
Pascal Lambert,Rayyan Khan,Marshall Pitz et al.
Pascal Lambert et al.
Background: Cancer progression is an important outcome in cancer research. However, it is frequently documented only in electronic health records (EHRs) as unstructured text, which requires lengthy and costly chart review...
Robust Cancer Crowdfunding Predictions: Leveraging Large Language Models and Machine Learning for Success Analysis [0.03%]
稳健的癌症众筹预测：利用大型语言模型和机器学习进行成功分析
Runa Bhaumik,Abhishikta Roy,Vineet Srivastava et al.
Runa Bhaumik et al.
Background: Recent advances in large language models (LLMs), such as GPT-4o, offer a transformative opportunity to extract nuanced linguistic, emotional, and social features from campaign texts at scale. These models enab...
Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review [0.03%]
基于环境监听和生成型人工智能的数字文员在临床文档工作流程中的真实世界证据综述：快速审查
Naga Sasidhar Kanaparthy,Yenny Villuendas-Rey,Tolulope Bakare et al.
Naga Sasidhar Kanaparthy et al.
Background: As physicians spend up to twice as much time on electronic health record tasks as on direct patient care, digital scribes have emerged as a promising solution to restore patient-clinician communication and red...
Review
JMIR AI. 2025 Oct 10:4:e76743. DOI:10.2196/76743 2025
Reinforcement Learning to Prevent Acute Care Events Among Medicaid Populations: Mixed Methods Study [0.03%]
面向医疗补助人群预防急性护理事件的强化学习研究：混合方法研究
Sanjay Basu,Bhairavi Muralidharan,Parth Sheth et al.
Sanjay Basu et al.
Background: Multidisciplinary care management teams must rapidly prioritize interventions for patients with complex medical and social needs. Current approaches rely on individual training, judgment, and experience, missi...
Assessing the Capability of Large Language Models for Navigation of the Australian Health Care System: Comparative Study [0.03%]
评估大型语言模型导航澳大利亚医疗保健系统能力的比较研究
Joshua Simmich,Megan Heather Ross,Trevor Glen Russell
Joshua Simmich
Background: Australians can face significant challenges in navigating the health care system, especially in rural and regional areas. Generative search tools, powered by large language models (LLMs), show promise in impro...
Developing a Tool for Identifying Clinical Risk From Free-Text Clinical Records: Natural Language Processing Study [0.03%]
一种识别自由文本临床记录中临床风险的工具开发：自然语言处理研究
Natasha Biscoe,Daniel Leightley,Dominic Murphy
Natasha Biscoe
Background: Electronic patient records are a valuable yet underused data source; they have been explored in research using natural language processing, but not yet within a third-sector organization. ...
Leveraging Smart Bed Technology to Detect COVID-19 Symptoms: Case Study [0.03%]
利用智能床技术检测COVID-19症状：案例研究
Gary Garcia-Molina,Dmytro Guzenko,Susan DeFranco et al.
Gary Garcia-Molina et al.
Background: Pathophysiological responses to viral infections such as COVID-19 significantly affect sleep duration, sleep quality, and concomitant cardiorespiratory function. The widespread adoption of consumer smart bed t...