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Background: As the global population ages, we witness a broad scientific and technological revolution tailored to meet the health challenges of older adults. Over the past 25 years, technological innovations, ranging from advanced medical devices to user-friendly mobile apps, are transforming the way we address these challenges, offering new avenues to enhance the quality of life and well-being of the aging demographic., Objective: This study aimed to systematically review the development trends in technology for managing and caring for the health of older adults over the past 25 years and to project future development prospects., Methods: We conducted a comprehensive bibliometric analysis of literatures related to technology-based solutions for health challenges in aging, published up to March 18, 2024. The search was performed using the Web of Science Core Collection, covering a span from 1999 to 2024. Our search strategy was designed to capture a broad spectrum of terms associated with aging, health challenges specific to older adults, and technological interventions., Results: A total of 1133 publications were found in the Web of Science Core Collection. The publication trend over these 25 years showed a gradual but fluctuating increase. The United States was the most productive country and participated in international collaboration most frequently. The predominant keywords identified through this analysis included "dementia," "telemedicine," "older-adults," "telehealth," and "care." The keywords with citation bursts included "telemedicine" and "digital health.", Conclusions: The scientific and technological revolution has significantly improved older adult health management, particularly in chronic disease monitoring, mobility, and social connectivity. The momentum for innovation continues to build, with future research likely to focus on predictive analytics and personalized health care solutions, further enhancing older adults' independence and quality of life., (©Lu Liu, Xiu-Ling Wang, Nuo Cheng, Fu-Min Yu, Hui-Jun Li, Yang Mu, Yonghui Yuan, Jia-Xin Dong, Yu-Dan Wu, Da-Xin Gong, Shuang Wang, Guang-Wei Zhang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.09.2024.)

Background: Internet hospitals (IHs) have rapidly developed as a promising strategy to address supply-demand imbalances in China's medical industry, with their capabilities directly dependent on information platform functionality. Moreover, a novel theory of "Trinity" smart hospital has provided advanced guidelines of IHs construction., Objective: To explore the construction experience, construction models, and development prospects based on operational data from IHs., Methods: Based on existing information systems and internet service functionalities, our hospital has built a "Smart Hospital Internet Information Platform (SHIIP)" for IHs operation, actively to expand online services, digitalize traditional healthcare, and explore healthcare services modes throughout the entire process and lifecycle. This article encompasses the platform architecture design, technological applications, patient service content and processes, healthcare professional support features, administrative management tools, and associated operational data., Results: Our platform has presented a remarkable set of data, including 82,279,669 visits, 420,120 online medical consultations, 124,422 electronic prescriptions, 92,285 medication deliveries, 6,965,566 pre-diagnosis triages, 4,995,824 offline outpatient appointments, 2,025 medical education articles with a total of 15,148,310 views, and so on. These data demonstrate the significant role of IH as an indispensable component of our physical hospital services, with a deep integration between online and offline healthcare systems., Conclusions: Attributing to extreme convenience and improved efficiency, our IH has achieved a wide recognition and use from both the public and healthcare workers, and the upward trends in multiple data metrics suggest a promising outlook for its sustained and positive development in the future. Our pioneering exploration holds tremendous significance and serves as a valuable guiding reference for IHs construction and the progressive development of the internet healthcare sector.

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners., (©Yu-Qing Cai, Da-Xin Gong, Li-Ying Tang, Yue Cai, Hui-Jun Li, Tian-Ci Jing, Mengchun Gong, Wei Hu, Zhen-Wei Zhang, Xingang Zhang, Guang-Wei Zhang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.07.2024.)