Regulatory Frameworks for AI-Enabled Medical Device Software in China: Comparative Analysis and Review of Implications for Global Manufacturer [0.03%]
中国人工智能医疗器械软件监管体系及其对国际制造商的影响分析与评估
Yu Han,Aaron Ceross,Jeroen Bergmann
Yu Han
The China State Council released the new generation artificial intelligence (AI) development plan, outlining China's ambitious aspiration to assume global leadership in AI by the year 2030. This initiative underscores the extensive applicab...
Use of Deep Neural Networks to Predict Obesity With Short Audio Recordings: Development and Usability Study [0.03%]
基于短音频录音使用深度神经网络预测肥胖的开发和适用性研究
Jingyi Huang,Peiqi Guo,Sheng Zhang et al.
Jingyi Huang et al.
Background: The escalating global prevalence of obesity has necessitated the exploration of novel diagnostic approaches. Recent scientific inquiries have indicated potential alterations in voice characteristics associated...
Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development [0.03%]
基于可解释机器学习模型预测血红蛋白A1C变化以提升2型糖尿病治疗决策：机器学习模型研发
Hisashi Kurasawa,Kayo Waki,Tomohisa Seki et al.
Hisashi Kurasawa et al.
Background: Type 2 diabetes (T2D) is a significant global health challenge. Physicians need to assess whether future glycemic control will be poor on the current trajectory of usual care and usual-care treatment intensifi...
Approaches for the use of Artificial Intelligence in workplace health promotion and prevention: A systematic scoping review [0.03%]
人工智促进职场健康促进与预防的应用方法：系统综述研究
Martin Lange,Alexandra Lowe,Ina Kayser et al.
Martin Lange et al.
Background: Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to imp...
Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study [0.03%]
针对可穿戴监测数据中不平衡的多尺度肠鸣音事件检测算法研发及验证研究
Annalisa Baronetto,Luisa Graf,Sarah Fischer et al.
Annalisa Baronetto et al.
Background: Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively. ...
Correction: Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation [0.03%]
纠正：使用GPT-4视觉的多模态人工智能分类中耳疾病的可能性：定性研究与验证
Masao Noda,Hidekane Yoshimura,Takuya Okubo et al.
Masao Noda et al.
[This corrects the article DOI: 10.2196/58342.]. ©Masao Noda, Hidekane Yoshimura, Takuya Okubo, Ryota Koshu, Yuki Uchiyama, Akihiro Nomura, Makoto Ito, ...
Published Erratum
JMIR AI. 2024 Jul 9:3:e62990. DOI:10.2196/62990 2024
Augmenting Telepostpartum Care With Vision-Based Detection of Breastfeeding-Related Conditions: Algorithm Development and Validation [0.03%]
基于视觉的检测算法在远程产后护理中的应用与发展：针对哺乳期相关症状的验证试验
Jessica De Souza,Varun Kumar Viswanath,Jessica Maria Echterhoff et al.
Jessica De Souza et al.
Background: Breastfeeding benefits both the mother and infant and is a topic of attention in public health. After childbirth, untreated medical conditions or lack of support lead many mothers to discontinue breastfeeding....
Health Care Professionals' and Parents' Perspectives on the Use of AI for Pain Monitoring in the Neonatal Intensive Care Unit: Multisite Qualitative Study [0.03%]
新生儿重症监护室人工智能疼痛监测的使用：医务人员和家长的多中心定性研究
Nicole Racine,Cheryl Chow,Lojain Hamwi et al.
Nicole Racine et al.
Background: The use of artificial intelligence (AI) for pain assessment has the potential to address historical challenges in infant pain assessment. There is a dearth of information on the perceived benefits and barriers...
Correction: Using Conversational AI to Facilitate Mental Health Assessments and Improve Clinical Efficiency Within Psychotherapy Services: Real-World Observational Study [0.03%]
纠正：使用对话式人工智能促进心理健康评估并改善心理治疗服务中的临床效率：真实世界观察性研究
Max Rollwage,Johanna Habicht,Keno Juechems et al.
Max Rollwage et al.
[This corrects the article DOI: 10.2196/44358.]. ©Max Rollwage, Johanna Habicht, Keno Juechems, Ben Carrington, Sruthi Viswanathan, Mona Stylianou, Tobi...
Published Erratum
JMIR AI. 2024 Mar 12:3:e57869. DOI:10.2196/57869 2024
Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation [0.03%]
具有隐私保护的联邦生存支持向量机在机构间时间结局分析的研发与验证算法
Julian Späth,Zeno Sewald,Niklas Probul et al.
Julian Späth et al.
Background: Central collection of distributed medical patient data is problematic due to strict privacy regulations. Especially in clinical environments, such as clinical time-to-event studies, large sample sizes are crit...