Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI

Application of Artificial Intelligence in Geriatric Care: Bibliometric Analysis

BACKGROUND

Artificial intelligence (AI) can improve the health and well-being of older adults and has the potential to assist and improve nursing care. In recent years, research in this area has been increasing. Therefore, it is necessary to understand the status of development and main research hotspots and identify the main contributors and their relationships in the application of AI in geriatric care via bibliometric analysis.

OBJECTIVE

Using bibliometric analysis, this study aims to examine the current research hotspots and collaborative networks in the application of AI in geriatric care over the past 23 years.

METHODS

The Web of Science Core Collection database was used as a source. All publications from inception to August 2022 were downloaded. The external characteristics of the publications were summarized through HistCite and the Web of Science. Keywords and collaborative networks were analyzed using VOSviewers and Citespace.

RESULTS

We obtained a total of 230 publications. The works originated in 499 institutions in 39 countries, were published in 124 journals, and were written by 1216 authors. Publications increased sharply from 2014 to 2022, accounting for 90.87% (209/230) of all publications. The United States and the International Journal of Social Robotics had the highest number of publications on this topic. The 1216 authors were divided into 5 main clusters. Among the 230 publications, 4 clusters were modeled, including Alzheimer disease, aged care, acceptance, and the surveillance and treatment of diseases. Machine learning, deep learning, and rehabilitation had also become recent research hotspots.

CONCLUSIONS

Research on the application of AI in geriatric care has developed rapidly. The development of research and cooperation among countries/regions and institutions are limited. In the future, strengthening the cooperation and communication between different countries/regions and institutions may further drive this field's development. This study provides researchers with the information necessary to understand the current state, collaborative networks, and main research hotspots of the field. In addition, our results suggest a series of recommendations for future research.

AI-enabled radiologist in the loop: novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia

A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.